import pandas as pd
import numpy as np
from datetime import timedelta
import datetime as dt
np.seterr(divide='ignore', invalid='ignore')
import plotly.express as px
import plotly.graph_objects as go
from ipyleaflet import *
import pycountry
#Create reference to CSV file
csv_path = "Resources/H1N1_2009.csv"
csv_path_2 = "Resources/covid_19_data.csv"
#Impor the CSV into a pandas DataFrame
h1n1 = pd.read_csv(csv_path, parse_dates=["Update Time"], encoding = 'unicode_escape')
covid = pd.read_csv(csv_path_2, parse_dates=["ObservationDate"])
#location_df
covid
| SNo | ObservationDate | Province/State | Country/Region | Last Update | Confirmed | Deaths | Recovered | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2020-01-22 | Anhui | Mainland China | 1/22/2020 17:00 | 1.0 | 0.0 | 0.0 |
| 1 | 2 | 2020-01-22 | Beijing | Mainland China | 1/22/2020 17:00 | 14.0 | 0.0 | 0.0 |
| 2 | 3 | 2020-01-22 | Chongqing | Mainland China | 1/22/2020 17:00 | 6.0 | 0.0 | 0.0 |
| 3 | 4 | 2020-01-22 | Fujian | Mainland China | 1/22/2020 17:00 | 1.0 | 0.0 | 0.0 |
| 4 | 5 | 2020-01-22 | Gansu | Mainland China | 1/22/2020 17:00 | 0.0 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 18641 | 18642 | 2020-04-25 | Wyoming | US | 2020-04-26 02:31:18 | 491.0 | 7.0 | 0.0 |
| 18642 | 18643 | 2020-04-25 | Xinjiang | Mainland China | 2020-04-26 02:31:18 | 76.0 | 3.0 | 73.0 |
| 18643 | 18644 | 2020-04-25 | Yukon | Canada | 2020-04-26 02:31:18 | 11.0 | 0.0 | 0.0 |
| 18644 | 18645 | 2020-04-25 | Yunnan | Mainland China | 2020-04-26 02:31:18 | 185.0 | 2.0 | 181.0 |
| 18645 | 18646 | 2020-04-25 | Zhejiang | Mainland China | 2020-04-26 02:31:18 | 1268.0 | 1.0 | 1257.0 |
18646 rows × 8 columns
covid = covid.loc[:,['ObservationDate', 'Province/State', 'Country/Region', 'Confirmed', 'Deaths', 'Recovered']]
#Rename Columns
covid = covid.rename(columns={"ObservationDate": "Date"})
covid = covid[['Province/State', 'Country/Region', 'Date', 'Confirmed', 'Deaths', 'Recovered']]
covid
| Province/State | Country/Region | Date | Confirmed | Deaths | Recovered | |
|---|---|---|---|---|---|---|
| 0 | Anhui | Mainland China | 2020-01-22 | 1.0 | 0.0 | 0.0 |
| 1 | Beijing | Mainland China | 2020-01-22 | 14.0 | 0.0 | 0.0 |
| 2 | Chongqing | Mainland China | 2020-01-22 | 6.0 | 0.0 | 0.0 |
| 3 | Fujian | Mainland China | 2020-01-22 | 1.0 | 0.0 | 0.0 |
| 4 | Gansu | Mainland China | 2020-01-22 | 0.0 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... |
| 18641 | Wyoming | US | 2020-04-25 | 491.0 | 7.0 | 0.0 |
| 18642 | Xinjiang | Mainland China | 2020-04-25 | 76.0 | 3.0 | 73.0 |
| 18643 | Yukon | Canada | 2020-04-25 | 11.0 | 0.0 | 0.0 |
| 18644 | Yunnan | Mainland China | 2020-04-25 | 185.0 | 2.0 | 181.0 |
| 18645 | Zhejiang | Mainland China | 2020-04-25 | 1268.0 | 1.0 | 1257.0 |
18646 rows × 6 columns
temp = covid
#Group Provinces and take largest cumulative confirmed and death number
province_df = temp.groupby(by='Province/State').agg('max').reset_index(drop=False)
#Group all provinces into their countries and add confirmed and death numbers
province_df = province_df.groupby(by='Country/Region').agg('sum').reset_index(drop=False)
province_df
| Country/Region | Confirmed | Deaths | Recovered | |
|---|---|---|---|---|
| 0 | Australia | 6694.0 | 80.0 | 5271.0 |
| 1 | Canada | 46357.0 | 2565.0 | 14.0 |
| 2 | Denmark | 1524.0 | 13.0 | 190.0 |
| 3 | France | 35456.0 | 1456.0 | 2949.0 |
| 4 | Germany | 5.0 | 0.0 | 0.0 |
| 5 | Hong Kong | 1037.0 | 4.0 | 753.0 |
| 6 | Israel | 8.0 | 0.0 | 0.0 |
| 7 | Lebanon | 2.0 | 0.0 | 0.0 |
| 8 | Macau | 45.0 | 0.0 | 28.0 |
| 9 | Mainland China | 82827.0 | 4632.0 | 78225.0 |
| 10 | Netherlands | 3825.0 | 151.0 | 104.0 |
| 11 | Others | 61.0 | 0.0 | 0.0 |
| 12 | Taiwan | 47.0 | 1.0 | 17.0 |
| 13 | UK | 6668.0 | 303.0 | 857.0 |
| 14 | US | 939634.0 | 53786.0 | 101141.0 |
#Confirmed, Deaths and Recovered are cumulative numbers per Province/State NOT by Country/Region
#Remove countries that are in province_df dataset
remove_list = province_df['Country/Region']
country_data = temp[~temp['Country/Region'].isin(remove_list)]
#province_df
country_data = country_data.loc[:,['Country/Region', 'Date', 'Confirmed', 'Deaths', 'Recovered']]
country_data = country_data.reset_index(drop=True)
country_data
| Country/Region | Date | Confirmed | Deaths | Recovered | |
|---|---|---|---|---|---|
| 0 | Japan | 2020-01-22 | 2.0 | 0.0 | 0.0 |
| 1 | Thailand | 2020-01-22 | 2.0 | 0.0 | 0.0 |
| 2 | South Korea | 2020-01-22 | 1.0 | 0.0 | 0.0 |
| 3 | Japan | 2020-01-23 | 1.0 | 0.0 | 0.0 |
| 4 | Thailand | 2020-01-23 | 3.0 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... |
| 8940 | West Bank and Gaza | 2020-04-25 | 342.0 | 2.0 | 92.0 |
| 8941 | Western Sahara | 2020-04-25 | 6.0 | 0.0 | 5.0 |
| 8942 | Yemen | 2020-04-25 | 1.0 | 0.0 | 1.0 |
| 8943 | Zambia | 2020-04-25 | 84.0 | 3.0 | 37.0 |
| 8944 | Zimbabwe | 2020-04-25 | 31.0 | 4.0 | 2.0 |
8945 rows × 5 columns
#Merge province and country data
complete_data = pd.concat([country_data, province_df], ignore_index=True)
complete_data
| Country/Region | Date | Confirmed | Deaths | Recovered | |
|---|---|---|---|---|---|
| 0 | Japan | 2020-01-22 | 2.0 | 0.0 | 0.0 |
| 1 | Thailand | 2020-01-22 | 2.0 | 0.0 | 0.0 |
| 2 | South Korea | 2020-01-22 | 1.0 | 0.0 | 0.0 |
| 3 | Japan | 2020-01-23 | 1.0 | 0.0 | 0.0 |
| 4 | Thailand | 2020-01-23 | 3.0 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... |
| 8955 | Netherlands | NaT | 3825.0 | 151.0 | 104.0 |
| 8956 | Others | NaT | 61.0 | 0.0 | 0.0 |
| 8957 | Taiwan | NaT | 47.0 | 1.0 | 17.0 |
| 8958 | UK | NaT | 6668.0 | 303.0 | 857.0 |
| 8959 | US | NaT | 939634.0 | 53786.0 | 101141.0 |
8960 rows × 5 columns
covid_2 = pd.read_csv(csv_path_2, parse_dates=["ObservationDate"])
covid_2 = covid_2.loc[:,['ObservationDate', 'Province/State', 'Country/Region', 'Confirmed', 'Deaths', 'Recovered']]
#Rename Columns
covid_2 = covid_2.rename(columns={"ObservationDate": "Date", "Province/State" : "Province", "Country/Region" : "Country"})
covid_2 = covid_2.groupby(['Date', 'Country'])[["Confirmed", "Deaths", "Recovered"]].sum().reset_index()
covid_2
| Date | Country | Confirmed | Deaths | Recovered | |
|---|---|---|---|---|---|
| 0 | 2020-01-22 | Hong Kong | 0.0 | 0.0 | 0.0 |
| 1 | 2020-01-22 | Japan | 2.0 | 0.0 | 0.0 |
| 2 | 2020-01-22 | Macau | 1.0 | 0.0 | 0.0 |
| 3 | 2020-01-22 | Mainland China | 547.0 | 17.0 | 28.0 |
| 4 | 2020-01-22 | South Korea | 1.0 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... |
| 10161 | 2020-04-25 | West Bank and Gaza | 342.0 | 2.0 | 92.0 |
| 10162 | 2020-04-25 | Western Sahara | 6.0 | 0.0 | 5.0 |
| 10163 | 2020-04-25 | Yemen | 1.0 | 0.0 | 1.0 |
| 10164 | 2020-04-25 | Zambia | 84.0 | 3.0 | 37.0 |
| 10165 | 2020-04-25 | Zimbabwe | 31.0 | 4.0 | 2.0 |
10166 rows × 5 columns
#Create Dataset that lists all countries
dfww = covid.loc[:,['Country/Region', 'Date', 'Confirmed', 'Deaths', 'Recovered']]
#Create DataFrame of days with Confirmed Cases
df1 = dfww[dfww['Confirmed']>0]
df1 = df1.reset_index(drop=True)
#Find start date of infections in YYY-DD-MM format
start_day = df1.groupby(by='Country/Region').agg('min').reset_index(drop=False)
start_day = start_day.loc[:,['Country/Region', 'Date']]
#Find todays date in YYYY-DD-MM format
today = dt.datetime.today()
#Days since first infection date
days_difference = []
days_difference = (today - start_day['Date']).dt.days
days_difference
#Merge on index
start_day = start_day.merge(days_difference, left_index=True, right_index=True)
start_day = start_day.rename(columns={'Country/Region': 'Country/Region',
'Date_x': 'Start Date',
'Date_y': 'Days Since 1st Case'})
start_day['Weeks Since 1st Case'] = round(start_day['Days Since 1st Case'] / 7, 2)
start_day.sort_values(by='Days Since 1st Case', ascending=False)\
.reset_index(drop=True).style.background_gradient(cmap='Reds')
#start_day
| Country/Region | Start Date | Days Since 1st Case | Weeks Since 1st Case | |
|---|---|---|---|---|
| 0 | Taiwan | 2020-01-22 00:00:00 | 95 | 13.570000 |
| 1 | Macau | 2020-01-22 00:00:00 | 95 | 13.570000 |
| 2 | Mainland China | 2020-01-22 00:00:00 | 95 | 13.570000 |
| 3 | Japan | 2020-01-22 00:00:00 | 95 | 13.570000 |
| 4 | Thailand | 2020-01-22 00:00:00 | 95 | 13.570000 |
| 5 | South Korea | 2020-01-22 00:00:00 | 95 | 13.570000 |
| 6 | US | 2020-01-22 00:00:00 | 95 | 13.570000 |
| 7 | Singapore | 2020-01-23 00:00:00 | 94 | 13.430000 |
| 8 | Vietnam | 2020-01-23 00:00:00 | 94 | 13.430000 |
| 9 | Hong Kong | 2020-01-23 00:00:00 | 94 | 13.430000 |
| 10 | France | 2020-01-24 00:00:00 | 93 | 13.290000 |
| 11 | Australia | 2020-01-25 00:00:00 | 92 | 13.140000 |
| 12 | Nepal | 2020-01-25 00:00:00 | 92 | 13.140000 |
| 13 | Malaysia | 2020-01-25 00:00:00 | 92 | 13.140000 |
| 14 | Canada | 2020-01-26 00:00:00 | 91 | 13.000000 |
| 15 | Cambodia | 2020-01-27 00:00:00 | 90 | 12.860000 |
| 16 | Sri Lanka | 2020-01-27 00:00:00 | 90 | 12.860000 |
| 17 | Ivory Coast | 2020-01-27 00:00:00 | 90 | 12.860000 |
| 18 | Germany | 2020-01-28 00:00:00 | 89 | 12.710000 |
| 19 | United Arab Emirates | 2020-01-29 00:00:00 | 88 | 12.570000 |
| 20 | Finland | 2020-01-29 00:00:00 | 88 | 12.570000 |
| 21 | India | 2020-01-30 00:00:00 | 87 | 12.430000 |
| 22 | Philippines | 2020-01-30 00:00:00 | 87 | 12.430000 |
| 23 | Sweden | 2020-01-31 00:00:00 | 86 | 12.290000 |
| 24 | Russia | 2020-01-31 00:00:00 | 86 | 12.290000 |
| 25 | UK | 2020-01-31 00:00:00 | 86 | 12.290000 |
| 26 | Italy | 2020-01-31 00:00:00 | 86 | 12.290000 |
| 27 | Spain | 2020-02-01 00:00:00 | 85 | 12.140000 |
| 28 | Belgium | 2020-02-04 00:00:00 | 82 | 11.710000 |
| 29 | Others | 2020-02-07 00:00:00 | 79 | 11.290000 |
| 30 | Egypt | 2020-02-14 00:00:00 | 72 | 10.290000 |
| 31 | Iran | 2020-02-19 00:00:00 | 67 | 9.570000 |
| 32 | Lebanon | 2020-02-21 00:00:00 | 65 | 9.290000 |
| 33 | Israel | 2020-02-21 00:00:00 | 65 | 9.290000 |
| 34 | Iraq | 2020-02-24 00:00:00 | 62 | 8.860000 |
| 35 | Afghanistan | 2020-02-24 00:00:00 | 62 | 8.860000 |
| 36 | Bahrain | 2020-02-24 00:00:00 | 62 | 8.860000 |
| 37 | Oman | 2020-02-24 00:00:00 | 62 | 8.860000 |
| 38 | Kuwait | 2020-02-24 00:00:00 | 62 | 8.860000 |
| 39 | Croatia | 2020-02-25 00:00:00 | 61 | 8.710000 |
| 40 | Algeria | 2020-02-25 00:00:00 | 61 | 8.710000 |
| 41 | Switzerland | 2020-02-25 00:00:00 | 61 | 8.710000 |
| 42 | Austria | 2020-02-25 00:00:00 | 61 | 8.710000 |
| 43 | Georgia | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 44 | Brazil | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 45 | North Macedonia | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 46 | Norway | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 47 | Romania | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 48 | Pakistan | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 49 | Greece | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 50 | San Marino | 2020-02-27 00:00:00 | 59 | 8.430000 |
| 51 | Estonia | 2020-02-27 00:00:00 | 59 | 8.430000 |
| 52 | Denmark | 2020-02-27 00:00:00 | 59 | 8.430000 |
| 53 | Netherlands | 2020-02-27 00:00:00 | 59 | 8.430000 |
| 54 | Lithuania | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 55 | Azerbaijan | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 56 | Belarus | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 57 | Iceland | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 58 | Mexico | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 59 | North Ireland | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 60 | Nigeria | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 61 | New Zealand | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 62 | Luxembourg | 2020-02-29 00:00:00 | 57 | 8.140000 |
| 63 | Qatar | 2020-02-29 00:00:00 | 57 | 8.140000 |
| 64 | Ireland | 2020-02-29 00:00:00 | 57 | 8.140000 |
| 65 | Monaco | 2020-02-29 00:00:00 | 57 | 8.140000 |
| 66 | Ecuador | 2020-03-01 00:00:00 | 56 | 8.000000 |
| 67 | Dominican Republic | 2020-03-01 00:00:00 | 56 | 8.000000 |
| 68 | Czech Republic | 2020-03-01 00:00:00 | 56 | 8.000000 |
| 69 | Armenia | 2020-03-01 00:00:00 | 56 | 8.000000 |
| 70 | Azerbaijan | 2020-03-01 00:00:00 | 56 | 8.000000 |
| 71 | Senegal | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 72 | Saudi Arabia | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 73 | Indonesia | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 74 | Morocco | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 75 | Portugal | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 76 | Latvia | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 77 | Andorra | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 78 | Jordan | 2020-03-03 00:00:00 | 54 | 7.710000 |
| 79 | Chile | 2020-03-03 00:00:00 | 54 | 7.710000 |
| 80 | Ukraine | 2020-03-03 00:00:00 | 54 | 7.710000 |
| 81 | Argentina | 2020-03-03 00:00:00 | 54 | 7.710000 |
| 82 | Tunisia | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 83 | Faroe Islands | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 84 | Hungary | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 85 | Liechtenstein | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 86 | Saint Barthelemy | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 87 | Poland | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 88 | Gibraltar | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 89 | Bosnia and Herzegovina | 2020-03-05 00:00:00 | 52 | 7.430000 |
| 90 | Slovenia | 2020-03-05 00:00:00 | 52 | 7.430000 |
| 91 | South Africa | 2020-03-05 00:00:00 | 52 | 7.430000 |
| 92 | Palestine | 2020-03-05 00:00:00 | 52 | 7.430000 |
| 93 | Vatican City | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 94 | Slovakia | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 95 | Togo | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 96 | Bhutan | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 97 | Colombia | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 98 | Peru | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 99 | Cameroon | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 100 | Serbia | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 101 | Costa Rica | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 102 | French Guiana | 2020-03-07 00:00:00 | 50 | 7.140000 |
| 103 | Martinique | 2020-03-07 00:00:00 | 50 | 7.140000 |
| 104 | Malta | 2020-03-07 00:00:00 | 50 | 7.140000 |
| 105 | Republic of Ireland | 2020-03-08 00:00:00 | 49 | 7.000000 |
| 106 | Bangladesh | 2020-03-08 00:00:00 | 49 | 7.000000 |
| 107 | Bulgaria | 2020-03-08 00:00:00 | 49 | 7.000000 |
| 108 | Maldives | 2020-03-08 00:00:00 | 49 | 7.000000 |
| 109 | Moldova | 2020-03-08 00:00:00 | 49 | 7.000000 |
| 110 | Paraguay | 2020-03-08 00:00:00 | 49 | 7.000000 |
| 111 | St. Martin | 2020-03-09 00:00:00 | 48 | 6.860000 |
| 112 | Cyprus | 2020-03-09 00:00:00 | 48 | 6.860000 |
| 113 | Albania | 2020-03-09 00:00:00 | 48 | 6.860000 |
| 114 | Brunei | 2020-03-09 00:00:00 | 48 | 6.860000 |
| 115 | Panama | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 116 | Mongolia | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 117 | occupied Palestinian territory | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 118 | Burkina Faso | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 119 | ('St. Martin',) | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 120 | Channel Islands | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 121 | Holy See | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 122 | Honduras | 2020-03-11 00:00:00 | 46 | 6.570000 |
| 123 | Congo (Kinshasa) | 2020-03-11 00:00:00 | 46 | 6.570000 |
| 124 | Turkey | 2020-03-11 00:00:00 | 46 | 6.570000 |
| 125 | Bolivia | 2020-03-11 00:00:00 | 46 | 6.570000 |
| 126 | Reunion | 2020-03-11 00:00:00 | 46 | 6.570000 |
| 127 | Jamaica | 2020-03-11 00:00:00 | 46 | 6.570000 |
| 128 | Cuba | 2020-03-12 00:00:00 | 45 | 6.430000 |
| 129 | Guyana | 2020-03-12 00:00:00 | 45 | 6.430000 |
| 130 | Aruba | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 131 | Cayman Islands | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 132 | Guinea | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 133 | Ethiopia | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 134 | Sudan | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 135 | Guadeloupe | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 136 | Kazakhstan | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 137 | Kenya | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 138 | Antigua and Barbuda | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 139 | Seychelles | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 140 | Eswatini | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 141 | Rwanda | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 142 | Gabon | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 143 | Saint Lucia | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 144 | Saint Vincent and the Grenadines | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 145 | Ghana | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 146 | Guatemala | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 147 | Uruguay | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 148 | Mauritania | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 149 | Guernsey | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 150 | Suriname | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 151 | Venezuela | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 152 | Namibia | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 153 | Trinidad and Tobago | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 154 | Curacao | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 155 | Jersey | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 156 | Equatorial Guinea | 2020-03-15 00:00:00 | 42 | 6.000000 |
| 157 | Uzbekistan | 2020-03-15 00:00:00 | 42 | 6.000000 |
| 158 | Central African Republic | 2020-03-15 00:00:00 | 42 | 6.000000 |
| 159 | Congo (Brazzaville) | 2020-03-15 00:00:00 | 42 | 6.000000 |
| 160 | Kosovo | 2020-03-15 00:00:00 | 42 | 6.000000 |
| 161 | Liberia | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 162 | Benin | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 163 | The Bahamas | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 164 | Guam | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 165 | Mayotte | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 166 | Republic of the Congo | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 167 | Tanzania | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 168 | Somalia | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 169 | Greenland | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 170 | Puerto Rico | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 171 | Montenegro | 2020-03-17 00:00:00 | 40 | 5.710000 |
| 172 | The Gambia | 2020-03-17 00:00:00 | 40 | 5.710000 |
| 173 | Barbados | 2020-03-17 00:00:00 | 40 | 5.710000 |
| 174 | Kyrgyzstan | 2020-03-18 00:00:00 | 39 | 5.570000 |
| 175 | Djibouti | 2020-03-18 00:00:00 | 39 | 5.570000 |
| 176 | Zambia | 2020-03-18 00:00:00 | 39 | 5.570000 |
| 177 | Gambia, The | 2020-03-18 00:00:00 | 39 | 5.570000 |
| 178 | Mauritius | 2020-03-18 00:00:00 | 39 | 5.570000 |
| 179 | El Salvador | 2020-03-19 00:00:00 | 38 | 5.430000 |
| 180 | Fiji | 2020-03-19 00:00:00 | 38 | 5.430000 |
| 181 | Chad | 2020-03-19 00:00:00 | 38 | 5.430000 |
| 182 | Nicaragua | 2020-03-19 00:00:00 | 38 | 5.430000 |
| 183 | Bahamas, The | 2020-03-19 00:00:00 | 38 | 5.430000 |
| 184 | Cabo Verde | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 185 | Angola | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 186 | Niger | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 187 | Zimbabwe | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 188 | Madagascar | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 189 | Papua New Guinea | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 190 | Haiti | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 191 | Uganda | 2020-03-21 00:00:00 | 36 | 5.140000 |
| 192 | Cape Verde | 2020-03-21 00:00:00 | 36 | 5.140000 |
| 193 | Eritrea | 2020-03-21 00:00:00 | 36 | 5.140000 |
| 194 | East Timor | 2020-03-21 00:00:00 | 36 | 5.140000 |
| 195 | Timor-Leste | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 196 | Grenada | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 197 | Mozambique | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 198 | Syria | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 199 | Dominica | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 200 | Bahamas | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 201 | Gambia | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 202 | Belize | 2020-03-23 00:00:00 | 34 | 4.860000 |
| 203 | Libya | 2020-03-24 00:00:00 | 33 | 4.710000 |
| 204 | Laos | 2020-03-24 00:00:00 | 33 | 4.710000 |
| 205 | Diamond Princess | 2020-03-25 00:00:00 | 32 | 4.570000 |
| 206 | Guinea-Bissau | 2020-03-25 00:00:00 | 32 | 4.570000 |
| 207 | Mali | 2020-03-25 00:00:00 | 32 | 4.570000 |
| 208 | Saint Kitts and Nevis | 2020-03-25 00:00:00 | 32 | 4.570000 |
| 209 | West Bank and Gaza | 2020-03-26 00:00:00 | 31 | 4.430000 |
| 210 | Burma | 2020-03-27 00:00:00 | 30 | 4.290000 |
| 211 | MS Zaandam | 2020-03-28 00:00:00 | 29 | 4.140000 |
| 212 | Botswana | 2020-03-30 00:00:00 | 27 | 3.860000 |
| 213 | Burundi | 2020-03-31 00:00:00 | 26 | 3.710000 |
| 214 | Sierra Leone | 2020-03-31 00:00:00 | 26 | 3.710000 |
| 215 | Malawi | 2020-04-02 00:00:00 | 24 | 3.430000 |
| 216 | South Sudan | 2020-04-05 00:00:00 | 21 | 3.000000 |
| 217 | Western Sahara | 2020-04-05 00:00:00 | 21 | 3.000000 |
| 218 | Sao Tome and Principe | 2020-04-06 00:00:00 | 20 | 2.860000 |
| 219 | Yemen | 2020-04-10 00:00:00 | 16 | 2.290000 |
start_day.sort_values(by='Days Since 1st Case', ascending=True)\
.reset_index(drop=True).style.background_gradient(cmap='Reds')
| Country/Region | Start Date | Days Since 1st Case | Weeks Since 1st Case | |
|---|---|---|---|---|
| 0 | Yemen | 2020-04-10 00:00:00 | 16 | 2.290000 |
| 1 | Sao Tome and Principe | 2020-04-06 00:00:00 | 20 | 2.860000 |
| 2 | South Sudan | 2020-04-05 00:00:00 | 21 | 3.000000 |
| 3 | Western Sahara | 2020-04-05 00:00:00 | 21 | 3.000000 |
| 4 | Malawi | 2020-04-02 00:00:00 | 24 | 3.430000 |
| 5 | Sierra Leone | 2020-03-31 00:00:00 | 26 | 3.710000 |
| 6 | Burundi | 2020-03-31 00:00:00 | 26 | 3.710000 |
| 7 | Botswana | 2020-03-30 00:00:00 | 27 | 3.860000 |
| 8 | MS Zaandam | 2020-03-28 00:00:00 | 29 | 4.140000 |
| 9 | Burma | 2020-03-27 00:00:00 | 30 | 4.290000 |
| 10 | West Bank and Gaza | 2020-03-26 00:00:00 | 31 | 4.430000 |
| 11 | Mali | 2020-03-25 00:00:00 | 32 | 4.570000 |
| 12 | Saint Kitts and Nevis | 2020-03-25 00:00:00 | 32 | 4.570000 |
| 13 | Diamond Princess | 2020-03-25 00:00:00 | 32 | 4.570000 |
| 14 | Guinea-Bissau | 2020-03-25 00:00:00 | 32 | 4.570000 |
| 15 | Libya | 2020-03-24 00:00:00 | 33 | 4.710000 |
| 16 | Laos | 2020-03-24 00:00:00 | 33 | 4.710000 |
| 17 | Belize | 2020-03-23 00:00:00 | 34 | 4.860000 |
| 18 | Bahamas | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 19 | Dominica | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 20 | Timor-Leste | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 21 | Mozambique | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 22 | Gambia | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 23 | Syria | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 24 | Grenada | 2020-03-22 00:00:00 | 35 | 5.000000 |
| 25 | East Timor | 2020-03-21 00:00:00 | 36 | 5.140000 |
| 26 | Eritrea | 2020-03-21 00:00:00 | 36 | 5.140000 |
| 27 | Uganda | 2020-03-21 00:00:00 | 36 | 5.140000 |
| 28 | Cape Verde | 2020-03-21 00:00:00 | 36 | 5.140000 |
| 29 | Cabo Verde | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 30 | Papua New Guinea | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 31 | Madagascar | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 32 | Angola | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 33 | Haiti | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 34 | Zimbabwe | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 35 | Niger | 2020-03-20 00:00:00 | 37 | 5.290000 |
| 36 | Bahamas, The | 2020-03-19 00:00:00 | 38 | 5.430000 |
| 37 | El Salvador | 2020-03-19 00:00:00 | 38 | 5.430000 |
| 38 | Chad | 2020-03-19 00:00:00 | 38 | 5.430000 |
| 39 | Fiji | 2020-03-19 00:00:00 | 38 | 5.430000 |
| 40 | Nicaragua | 2020-03-19 00:00:00 | 38 | 5.430000 |
| 41 | Mauritius | 2020-03-18 00:00:00 | 39 | 5.570000 |
| 42 | Kyrgyzstan | 2020-03-18 00:00:00 | 39 | 5.570000 |
| 43 | Djibouti | 2020-03-18 00:00:00 | 39 | 5.570000 |
| 44 | Zambia | 2020-03-18 00:00:00 | 39 | 5.570000 |
| 45 | Gambia, The | 2020-03-18 00:00:00 | 39 | 5.570000 |
| 46 | The Gambia | 2020-03-17 00:00:00 | 40 | 5.710000 |
| 47 | Barbados | 2020-03-17 00:00:00 | 40 | 5.710000 |
| 48 | Montenegro | 2020-03-17 00:00:00 | 40 | 5.710000 |
| 49 | Greenland | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 50 | Guam | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 51 | Tanzania | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 52 | Liberia | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 53 | The Bahamas | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 54 | Somalia | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 55 | Puerto Rico | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 56 | Republic of the Congo | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 57 | Benin | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 58 | Mayotte | 2020-03-16 00:00:00 | 41 | 5.860000 |
| 59 | Uzbekistan | 2020-03-15 00:00:00 | 42 | 6.000000 |
| 60 | Equatorial Guinea | 2020-03-15 00:00:00 | 42 | 6.000000 |
| 61 | Central African Republic | 2020-03-15 00:00:00 | 42 | 6.000000 |
| 62 | Congo (Brazzaville) | 2020-03-15 00:00:00 | 42 | 6.000000 |
| 63 | Kosovo | 2020-03-15 00:00:00 | 42 | 6.000000 |
| 64 | Suriname | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 65 | Jersey | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 66 | Guernsey | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 67 | Guatemala | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 68 | Rwanda | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 69 | Venezuela | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 70 | Ghana | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 71 | Saint Lucia | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 72 | Curacao | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 73 | Gabon | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 74 | Trinidad and Tobago | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 75 | Mauritania | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 76 | Uruguay | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 77 | Seychelles | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 78 | Eswatini | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 79 | Namibia | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 80 | Saint Vincent and the Grenadines | 2020-03-14 00:00:00 | 43 | 6.140000 |
| 81 | Kazakhstan | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 82 | Sudan | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 83 | Kenya | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 84 | Antigua and Barbuda | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 85 | Cayman Islands | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 86 | Ethiopia | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 87 | Guadeloupe | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 88 | Aruba | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 89 | Guinea | 2020-03-13 00:00:00 | 44 | 6.290000 |
| 90 | Guyana | 2020-03-12 00:00:00 | 45 | 6.430000 |
| 91 | Cuba | 2020-03-12 00:00:00 | 45 | 6.430000 |
| 92 | Jamaica | 2020-03-11 00:00:00 | 46 | 6.570000 |
| 93 | Honduras | 2020-03-11 00:00:00 | 46 | 6.570000 |
| 94 | Bolivia | 2020-03-11 00:00:00 | 46 | 6.570000 |
| 95 | Reunion | 2020-03-11 00:00:00 | 46 | 6.570000 |
| 96 | Congo (Kinshasa) | 2020-03-11 00:00:00 | 46 | 6.570000 |
| 97 | Turkey | 2020-03-11 00:00:00 | 46 | 6.570000 |
| 98 | Mongolia | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 99 | Panama | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 100 | occupied Palestinian territory | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 101 | ('St. Martin',) | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 102 | Holy See | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 103 | Burkina Faso | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 104 | Channel Islands | 2020-03-10 00:00:00 | 47 | 6.710000 |
| 105 | Cyprus | 2020-03-09 00:00:00 | 48 | 6.860000 |
| 106 | Albania | 2020-03-09 00:00:00 | 48 | 6.860000 |
| 107 | St. Martin | 2020-03-09 00:00:00 | 48 | 6.860000 |
| 108 | Brunei | 2020-03-09 00:00:00 | 48 | 6.860000 |
| 109 | Paraguay | 2020-03-08 00:00:00 | 49 | 7.000000 |
| 110 | Republic of Ireland | 2020-03-08 00:00:00 | 49 | 7.000000 |
| 111 | Bangladesh | 2020-03-08 00:00:00 | 49 | 7.000000 |
| 112 | Maldives | 2020-03-08 00:00:00 | 49 | 7.000000 |
| 113 | Bulgaria | 2020-03-08 00:00:00 | 49 | 7.000000 |
| 114 | Moldova | 2020-03-08 00:00:00 | 49 | 7.000000 |
| 115 | Malta | 2020-03-07 00:00:00 | 50 | 7.140000 |
| 116 | Martinique | 2020-03-07 00:00:00 | 50 | 7.140000 |
| 117 | French Guiana | 2020-03-07 00:00:00 | 50 | 7.140000 |
| 118 | Bhutan | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 119 | Vatican City | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 120 | Costa Rica | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 121 | Cameroon | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 122 | Slovakia | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 123 | Colombia | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 124 | Peru | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 125 | Serbia | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 126 | Togo | 2020-03-06 00:00:00 | 51 | 7.290000 |
| 127 | Bosnia and Herzegovina | 2020-03-05 00:00:00 | 52 | 7.430000 |
| 128 | South Africa | 2020-03-05 00:00:00 | 52 | 7.430000 |
| 129 | Palestine | 2020-03-05 00:00:00 | 52 | 7.430000 |
| 130 | Slovenia | 2020-03-05 00:00:00 | 52 | 7.430000 |
| 131 | Hungary | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 132 | Saint Barthelemy | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 133 | Gibraltar | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 134 | Faroe Islands | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 135 | Poland | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 136 | Tunisia | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 137 | Liechtenstein | 2020-03-04 00:00:00 | 53 | 7.570000 |
| 138 | Chile | 2020-03-03 00:00:00 | 54 | 7.710000 |
| 139 | Ukraine | 2020-03-03 00:00:00 | 54 | 7.710000 |
| 140 | Argentina | 2020-03-03 00:00:00 | 54 | 7.710000 |
| 141 | Jordan | 2020-03-03 00:00:00 | 54 | 7.710000 |
| 142 | Saudi Arabia | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 143 | Morocco | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 144 | Latvia | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 145 | Indonesia | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 146 | Senegal | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 147 | Portugal | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 148 | Andorra | 2020-03-02 00:00:00 | 55 | 7.860000 |
| 149 | Armenia | 2020-03-01 00:00:00 | 56 | 8.000000 |
| 150 | Azerbaijan | 2020-03-01 00:00:00 | 56 | 8.000000 |
| 151 | Dominican Republic | 2020-03-01 00:00:00 | 56 | 8.000000 |
| 152 | Czech Republic | 2020-03-01 00:00:00 | 56 | 8.000000 |
| 153 | Ecuador | 2020-03-01 00:00:00 | 56 | 8.000000 |
| 154 | Luxembourg | 2020-02-29 00:00:00 | 57 | 8.140000 |
| 155 | Ireland | 2020-02-29 00:00:00 | 57 | 8.140000 |
| 156 | Qatar | 2020-02-29 00:00:00 | 57 | 8.140000 |
| 157 | Monaco | 2020-02-29 00:00:00 | 57 | 8.140000 |
| 158 | Azerbaijan | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 159 | Iceland | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 160 | Belarus | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 161 | North Ireland | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 162 | Nigeria | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 163 | New Zealand | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 164 | Lithuania | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 165 | Mexico | 2020-02-28 00:00:00 | 58 | 8.290000 |
| 166 | Denmark | 2020-02-27 00:00:00 | 59 | 8.430000 |
| 167 | Estonia | 2020-02-27 00:00:00 | 59 | 8.430000 |
| 168 | Netherlands | 2020-02-27 00:00:00 | 59 | 8.430000 |
| 169 | San Marino | 2020-02-27 00:00:00 | 59 | 8.430000 |
| 170 | Brazil | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 171 | Georgia | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 172 | Greece | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 173 | Pakistan | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 174 | Norway | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 175 | North Macedonia | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 176 | Romania | 2020-02-26 00:00:00 | 60 | 8.570000 |
| 177 | Croatia | 2020-02-25 00:00:00 | 61 | 8.710000 |
| 178 | Algeria | 2020-02-25 00:00:00 | 61 | 8.710000 |
| 179 | Austria | 2020-02-25 00:00:00 | 61 | 8.710000 |
| 180 | Switzerland | 2020-02-25 00:00:00 | 61 | 8.710000 |
| 181 | Kuwait | 2020-02-24 00:00:00 | 62 | 8.860000 |
| 182 | Oman | 2020-02-24 00:00:00 | 62 | 8.860000 |
| 183 | Bahrain | 2020-02-24 00:00:00 | 62 | 8.860000 |
| 184 | Iraq | 2020-02-24 00:00:00 | 62 | 8.860000 |
| 185 | Afghanistan | 2020-02-24 00:00:00 | 62 | 8.860000 |
| 186 | Lebanon | 2020-02-21 00:00:00 | 65 | 9.290000 |
| 187 | Israel | 2020-02-21 00:00:00 | 65 | 9.290000 |
| 188 | Iran | 2020-02-19 00:00:00 | 67 | 9.570000 |
| 189 | Egypt | 2020-02-14 00:00:00 | 72 | 10.290000 |
| 190 | Others | 2020-02-07 00:00:00 | 79 | 11.290000 |
| 191 | Belgium | 2020-02-04 00:00:00 | 82 | 11.710000 |
| 192 | Spain | 2020-02-01 00:00:00 | 85 | 12.140000 |
| 193 | Russia | 2020-01-31 00:00:00 | 86 | 12.290000 |
| 194 | Italy | 2020-01-31 00:00:00 | 86 | 12.290000 |
| 195 | Sweden | 2020-01-31 00:00:00 | 86 | 12.290000 |
| 196 | UK | 2020-01-31 00:00:00 | 86 | 12.290000 |
| 197 | Philippines | 2020-01-30 00:00:00 | 87 | 12.430000 |
| 198 | India | 2020-01-30 00:00:00 | 87 | 12.430000 |
| 199 | Finland | 2020-01-29 00:00:00 | 88 | 12.570000 |
| 200 | United Arab Emirates | 2020-01-29 00:00:00 | 88 | 12.570000 |
| 201 | Germany | 2020-01-28 00:00:00 | 89 | 12.710000 |
| 202 | Ivory Coast | 2020-01-27 00:00:00 | 90 | 12.860000 |
| 203 | Sri Lanka | 2020-01-27 00:00:00 | 90 | 12.860000 |
| 204 | Cambodia | 2020-01-27 00:00:00 | 90 | 12.860000 |
| 205 | Canada | 2020-01-26 00:00:00 | 91 | 13.000000 |
| 206 | Australia | 2020-01-25 00:00:00 | 92 | 13.140000 |
| 207 | Nepal | 2020-01-25 00:00:00 | 92 | 13.140000 |
| 208 | Malaysia | 2020-01-25 00:00:00 | 92 | 13.140000 |
| 209 | France | 2020-01-24 00:00:00 | 93 | 13.290000 |
| 210 | Singapore | 2020-01-23 00:00:00 | 94 | 13.430000 |
| 211 | Hong Kong | 2020-01-23 00:00:00 | 94 | 13.430000 |
| 212 | Vietnam | 2020-01-23 00:00:00 | 94 | 13.430000 |
| 213 | Mainland China | 2020-01-22 00:00:00 | 95 | 13.570000 |
| 214 | Taiwan | 2020-01-22 00:00:00 | 95 | 13.570000 |
| 215 | South Korea | 2020-01-22 00:00:00 | 95 | 13.570000 |
| 216 | Japan | 2020-01-22 00:00:00 | 95 | 13.570000 |
| 217 | Macau | 2020-01-22 00:00:00 | 95 | 13.570000 |
| 218 | US | 2020-01-22 00:00:00 | 95 | 13.570000 |
| 219 | Thailand | 2020-01-22 00:00:00 | 95 | 13.570000 |
#Store Country, Days Since 1st Case and Weeks Since 1st Case
start_day = start_day.loc[:, ['Country/Region', 'Days Since 1st Case', 'Weeks Since 1st Case']]
start_day
| Country/Region | Days Since 1st Case | Weeks Since 1st Case | |
|---|---|---|---|
| 0 | Azerbaijan | 58 | 8.29 |
| 1 | ('St. Martin',) | 47 | 6.71 |
| 2 | Afghanistan | 62 | 8.86 |
| 3 | Albania | 48 | 6.86 |
| 4 | Algeria | 61 | 8.71 |
| ... | ... | ... | ... |
| 215 | Western Sahara | 21 | 3.00 |
| 216 | Yemen | 16 | 2.29 |
| 217 | Zambia | 39 | 5.57 |
| 218 | Zimbabwe | 37 | 5.29 |
| 219 | occupied Palestinian territory | 47 | 6.71 |
220 rows × 3 columns
#Merge datasets together so age metrics follow global analysis
df5 = complete_data.groupby(by='Country/Region').agg('max').reset_index(drop=False)
df5 = pd.merge(df5, start_day, on='Country/Region')
#Add Column for "Active" = 'Confirmed' - 'Deaths' - 'Recovered'
active = df5['Confirmed'] - df5['Deaths'] - df5['Recovered']
df5['Active'] = active
df5 = df5[['Country/Region', 'Date',
'Confirmed', 'Deaths',
'Recovered', 'Active',
'Date', 'Days Since 1st Case',
'Weeks Since 1st Case'
]]
df5 = df5.loc[:,['Country/Region', 'Confirmed', 'Deaths', 'Recovered', 'Active', 'Days Since 1st Case', 'Weeks Since 1st Case']]
df5
| Country/Region | Confirmed | Deaths | Recovered | Active | Days Since 1st Case | Weeks Since 1st Case | |
|---|---|---|---|---|---|---|---|
| 0 | Azerbaijan | 1.0 | 0.0 | 0.0 | 1.0 | 58 | 8.29 |
| 1 | ('St. Martin',) | 2.0 | 0.0 | 0.0 | 2.0 | 47 | 6.71 |
| 2 | Afghanistan | 1463.0 | 47.0 | 188.0 | 1228.0 | 62 | 8.86 |
| 3 | Albania | 712.0 | 27.0 | 403.0 | 282.0 | 48 | 6.86 |
| 4 | Algeria | 3256.0 | 419.0 | 1479.0 | 1358.0 | 61 | 8.71 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 215 | Western Sahara | 6.0 | 0.0 | 5.0 | 1.0 | 21 | 3.00 |
| 216 | Yemen | 1.0 | 0.0 | 1.0 | 0.0 | 16 | 2.29 |
| 217 | Zambia | 84.0 | 3.0 | 37.0 | 44.0 | 39 | 5.57 |
| 218 | Zimbabwe | 31.0 | 4.0 | 2.0 | 25.0 | 37 | 5.29 |
| 219 | occupied Palestinian territory | 25.0 | 0.0 | 0.0 | 25.0 | 47 | 6.71 |
220 rows × 7 columns
dfww_confirmed = df5.sort_values(by='Confirmed', ascending=False).reset_index(drop=True)
dfww_confirmed.style.background_gradient(cmap='YlOrRd').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
'Recovered': '{:.0f}', 'Active': '{:.0f}',
'Weeks Since 1st Case': '{:.2f}'})
#dfww_confirmed
| Country/Region | Confirmed | Deaths | Recovered | Active | Days Since 1st Case | Weeks Since 1st Case | |
|---|---|---|---|---|---|---|---|
| 0 | US | 939634 | 53786 | 101141 | 784707 | 95 | 13.57 |
| 1 | Spain | 223759 | 22902 | 95708 | 105149 | 85 | 12.14 |
| 2 | Italy | 195351 | 26384 | 63120 | 105847 | 86 | 12.29 |
| 3 | Turkey | 107773 | 2706 | 25582 | 79485 | 46 | 6.57 |
| 4 | Iran | 89328 | 5650 | 68193 | 15485 | 67 | 9.57 |
| 5 | Mainland China | 82827 | 4632 | 78225 | -30 | 95 | 13.57 |
| 6 | Russia | 74588 | 681 | 6250 | 67657 | 86 | 12.29 |
| 7 | Brazil | 59324 | 4057 | 29160 | 26107 | 60 | 8.57 |
| 8 | Canada | 46357 | 2565 | 14 | 43778 | 91 | 13.00 |
| 9 | Belgium | 45325 | 6917 | 10417 | 27991 | 82 | 11.71 |
| 10 | France | 35456 | 1456 | 2949 | 31051 | 93 | 13.29 |
| 11 | Switzerland | 28894 | 1599 | 21300 | 5995 | 61 | 8.71 |
| 12 | India | 26283 | 825 | 5939 | 19519 | 87 | 12.43 |
| 13 | Peru | 25331 | 700 | 7797 | 16834 | 51 | 7.29 |
| 14 | Portugal | 23392 | 880 | 1277 | 21235 | 55 | 7.86 |
| 15 | Ecuador | 22719 | 576 | 1366 | 20777 | 56 | 8.00 |
| 16 | Ireland | 18561 | 1063 | 9233 | 8265 | 57 | 8.14 |
| 17 | Sweden | 18177 | 2192 | 1005 | 14980 | 86 | 12.29 |
| 18 | Saudi Arabia | 16299 | 136 | 2215 | 13948 | 55 | 7.86 |
| 19 | Austria | 15148 | 536 | 12103 | 2509 | 61 | 8.71 |
| 20 | Mexico | 13842 | 1305 | 7149 | 5388 | 58 | 8.29 |
| 21 | Japan | 13231 | 360 | 1656 | 11215 | 95 | 13.57 |
| 22 | Chile | 12858 | 181 | 6746 | 5931 | 54 | 7.71 |
| 23 | Pakistan | 12723 | 269 | 2866 | 9588 | 60 | 8.57 |
| 24 | Singapore | 12693 | 12 | 1002 | 11679 | 94 | 13.43 |
| 25 | Poland | 11273 | 524 | 2126 | 8623 | 53 | 7.57 |
| 26 | South Korea | 10728 | 242 | 8717 | 1769 | 95 | 13.57 |
| 27 | Romania | 10635 | 601 | 2890 | 7144 | 60 | 8.57 |
| 28 | United Arab Emirates | 9813 | 71 | 1887 | 7855 | 88 | 12.57 |
| 29 | Belarus | 9590 | 67 | 1573 | 7950 | 58 | 8.29 |
| 30 | Qatar | 9358 | 10 | 929 | 8419 | 57 | 8.14 |
| 31 | Indonesia | 8607 | 720 | 1042 | 6845 | 55 | 7.86 |
| 32 | Ukraine | 8125 | 201 | 782 | 7142 | 54 | 7.71 |
| 33 | Norway | 7499 | 201 | 32 | 7266 | 60 | 8.57 |
| 34 | Czech Republic | 7352 | 218 | 2453 | 4681 | 56 | 8.00 |
| 35 | Philippines | 7294 | 494 | 792 | 6008 | 87 | 12.43 |
| 36 | Australia | 6694 | 80 | 5271 | 1343 | 92 | 13.14 |
| 37 | UK | 6668 | 303 | 857 | 5508 | 86 | 12.29 |
| 38 | Serbia | 6630 | 125 | 870 | 5635 | 51 | 7.29 |
| 39 | Dominican Republic | 5926 | 273 | 822 | 4831 | 56 | 8.00 |
| 40 | Malaysia | 5742 | 98 | 3762 | 1882 | 92 | 13.14 |
| 41 | Panama | 5538 | 159 | 338 | 5041 | 47 | 6.71 |
| 42 | Colombia | 5142 | 233 | 1067 | 3842 | 51 | 7.29 |
| 43 | Bangladesh | 4998 | 140 | 113 | 4745 | 49 | 7.00 |
| 44 | Finland | 4475 | 186 | 2500 | 1789 | 88 | 12.57 |
| 45 | South Africa | 4361 | 86 | 1473 | 2802 | 52 | 7.43 |
| 46 | Egypt | 4319 | 307 | 1114 | 2898 | 72 | 10.29 |
| 47 | Morocco | 3897 | 159 | 537 | 3201 | 55 | 7.86 |
| 48 | Netherlands | 3825 | 151 | 104 | 3570 | 59 | 8.43 |
| 49 | Argentina | 3780 | 185 | 1030 | 2565 | 54 | 7.71 |
| 50 | Luxembourg | 3711 | 85 | 3088 | 538 | 57 | 8.14 |
| 51 | Moldova | 3304 | 94 | 825 | 2385 | 49 | 7.00 |
| 52 | Algeria | 3256 | 419 | 1479 | 1358 | 61 | 8.71 |
| 53 | Thailand | 2907 | 51 | 2547 | 309 | 95 | 13.57 |
| 54 | Kuwait | 2892 | 19 | 656 | 2217 | 62 | 8.86 |
| 55 | Kazakhstan | 2601 | 25 | 646 | 1930 | 44 | 6.29 |
| 56 | Bahrain | 2588 | 8 | 1160 | 1420 | 62 | 8.86 |
| 57 | Greece | 2506 | 130 | 577 | 1799 | 60 | 8.57 |
| 58 | Hungary | 2443 | 262 | 458 | 1723 | 53 | 7.57 |
| 59 | Croatia | 2016 | 54 | 1034 | 928 | 61 | 8.71 |
| 60 | Oman | 1905 | 10 | 329 | 1566 | 62 | 8.86 |
| 61 | Uzbekistan | 1862 | 8 | 707 | 1147 | 42 | 6.00 |
| 62 | Iceland | 1790 | 10 | 1570 | 210 | 58 | 8.29 |
| 63 | Iraq | 1763 | 86 | 1224 | 453 | 62 | 8.86 |
| 64 | Armenia | 1677 | 28 | 803 | 846 | 56 | 8.00 |
| 65 | Estonia | 1635 | 46 | 228 | 1361 | 59 | 8.43 |
| 66 | Azerbaijan | 1617 | 21 | 1080 | 516 | 56 | 8.00 |
| 67 | Denmark | 1524 | 13 | 190 | 1321 | 59 | 8.43 |
| 68 | Cameroon | 1518 | 53 | 697 | 768 | 51 | 7.29 |
| 69 | Bosnia and Herzegovina | 1486 | 57 | 592 | 837 | 52 | 7.43 |
| 70 | New Zealand | 1470 | 18 | 1142 | 310 | 58 | 8.29 |
| 71 | Afghanistan | 1463 | 47 | 188 | 1228 | 62 | 8.86 |
| 72 | Lithuania | 1426 | 41 | 460 | 925 | 58 | 8.29 |
| 73 | Slovenia | 1388 | 81 | 219 | 1088 | 52 | 7.43 |
| 74 | Slovakia | 1373 | 17 | 386 | 970 | 51 | 7.29 |
| 75 | North Macedonia | 1367 | 59 | 374 | 934 | 60 | 8.57 |
| 76 | Cuba | 1337 | 51 | 437 | 849 | 45 | 6.43 |
| 77 | Ghana | 1279 | 10 | 134 | 1135 | 43 | 6.14 |
| 78 | Bulgaria | 1247 | 55 | 197 | 995 | 49 | 7.00 |
| 79 | Nigeria | 1182 | 35 | 222 | 925 | 58 | 8.29 |
| 80 | Ivory Coast | 1077 | 14 | 419 | 644 | 90 | 12.86 |
| 81 | Hong Kong | 1037 | 4 | 753 | 280 | 94 | 13.43 |
| 82 | Djibouti | 1008 | 2 | 373 | 633 | 39 | 5.57 |
| 83 | Guinea | 996 | 7 | 208 | 781 | 44 | 6.29 |
| 84 | Tunisia | 939 | 38 | 207 | 694 | 53 | 7.57 |
| 85 | Bolivia | 866 | 46 | 54 | 766 | 46 | 6.57 |
| 86 | Cyprus | 810 | 14 | 148 | 648 | 48 | 6.86 |
| 87 | Latvia | 804 | 12 | 267 | 525 | 55 | 7.86 |
| 88 | Andorra | 738 | 40 | 344 | 354 | 55 | 7.86 |
| 89 | Albania | 712 | 27 | 403 | 282 | 48 | 6.86 |
| 90 | Diamond Princess | 712 | 13 | 645 | 54 | 32 | 4.57 |
| 91 | Costa Rica | 693 | 6 | 242 | 445 | 51 | 7.29 |
| 92 | Niger | 684 | 27 | 325 | 332 | 37 | 5.29 |
| 93 | Kyrgyzstan | 665 | 8 | 345 | 312 | 39 | 5.57 |
| 94 | Burkina Faso | 629 | 41 | 442 | 146 | 47 | 6.71 |
| 95 | Honduras | 627 | 59 | 65 | 503 | 46 | 6.57 |
| 96 | Senegal | 614 | 7 | 276 | 331 | 55 | 7.86 |
| 97 | Uruguay | 596 | 14 | 370 | 212 | 43 | 6.14 |
| 98 | San Marino | 513 | 40 | 64 | 409 | 59 | 8.43 |
| 99 | Kosovo | 510 | 12 | 93 | 405 | 42 | 6.00 |
| 100 | West Bank and Gaza | 484 | 4 | 92 | 388 | 31 | 4.43 |
| 101 | Guatemala | 473 | 13 | 45 | 415 | 43 | 6.14 |
| 102 | Sri Lanka | 460 | 7 | 118 | 335 | 90 | 12.86 |
| 103 | Georgia | 456 | 5 | 139 | 312 | 60 | 8.57 |
| 104 | Malta | 448 | 4 | 249 | 195 | 50 | 7.14 |
| 105 | Jordan | 444 | 7 | 332 | 105 | 54 | 7.71 |
| 106 | Congo (Kinshasa) | 416 | 28 | 49 | 339 | 46 | 6.57 |
| 107 | Somalia | 390 | 18 | 8 | 364 | 41 | 5.86 |
| 108 | Mali | 370 | 21 | 91 | 258 | 32 | 4.57 |
| 109 | Kenya | 343 | 14 | 98 | 231 | 44 | 6.29 |
| 110 | Mauritius | 331 | 9 | 295 | 27 | 39 | 5.57 |
| 111 | Venezuela | 323 | 10 | 132 | 181 | 43 | 6.14 |
| 112 | Montenegro | 320 | 6 | 153 | 161 | 40 | 5.71 |
| 113 | Jamaica | 305 | 7 | 28 | 270 | 46 | 6.57 |
| 114 | Tanzania | 299 | 10 | 48 | 241 | 41 | 5.86 |
| 115 | El Salvador | 274 | 8 | 75 | 191 | 38 | 5.43 |
| 116 | Vietnam | 270 | 0 | 225 | 45 | 94 | 13.43 |
| 117 | Equatorial Guinea | 258 | 1 | 7 | 250 | 42 | 6.00 |
| 118 | Paraguay | 228 | 9 | 85 | 134 | 49 | 7.00 |
| 119 | Sudan | 213 | 17 | 19 | 177 | 44 | 6.29 |
| 120 | Congo (Brazzaville) | 200 | 6 | 19 | 175 | 42 | 6.00 |
| 121 | Rwanda | 183 | 0 | 88 | 95 | 43 | 6.14 |
| 122 | Maldives | 177 | 0 | 17 | 160 | 49 | 7.00 |
| 123 | Gabon | 176 | 3 | 30 | 143 | 43 | 6.14 |
| 124 | Burma | 146 | 5 | 10 | 131 | 30 | 4.29 |
| 125 | Brunei | 138 | 1 | 121 | 16 | 48 | 6.86 |
| 126 | Madagascar | 123 | 0 | 62 | 61 | 37 | 5.29 |
| 127 | Cambodia | 122 | 0 | 117 | 5 | 90 | 12.86 |
| 128 | Ethiopia | 122 | 3 | 29 | 90 | 44 | 6.29 |
| 129 | Liberia | 120 | 11 | 25 | 84 | 41 | 5.86 |
| 130 | Trinidad and Tobago | 115 | 8 | 53 | 54 | 43 | 6.14 |
| 131 | Togo | 96 | 6 | 62 | 28 | 51 | 7.29 |
| 132 | Monaco | 94 | 4 | 42 | 48 | 57 | 8.14 |
| 133 | Cabo Verde | 90 | 1 | 1 | 88 | 37 | 5.29 |
| 134 | Zambia | 84 | 3 | 37 | 44 | 39 | 5.57 |
| 135 | Sierra Leone | 82 | 2 | 10 | 70 | 26 | 3.71 |
| 136 | Liechtenstein | 81 | 1 | 55 | 25 | 53 | 7.57 |
| 137 | Barbados | 79 | 6 | 31 | 42 | 40 | 5.71 |
| 138 | Bahamas | 78 | 11 | 15 | 52 | 35 | 5.00 |
| 139 | Uganda | 75 | 0 | 46 | 29 | 36 | 5.14 |
| 140 | Guyana | 73 | 7 | 12 | 54 | 45 | 6.43 |
| 141 | Haiti | 72 | 6 | 6 | 60 | 37 | 5.29 |
| 142 | Mozambique | 70 | 0 | 12 | 58 | 35 | 5.00 |
| 143 | Others | 61 | 0 | 0 | 61 | 79 | 11.29 |
| 144 | Libya | 61 | 2 | 18 | 41 | 33 | 4.71 |
| 145 | Eswatini | 56 | 1 | 10 | 45 | 43 | 6.14 |
| 146 | Benin | 54 | 1 | 27 | 26 | 41 | 5.86 |
| 147 | Guadeloupe | 53 | 0 | 0 | 53 | 44 | 6.29 |
| 148 | Guinea-Bissau | 52 | 0 | 3 | 49 | 32 | 4.57 |
| 149 | Nepal | 49 | 0 | 12 | 37 | 92 | 13.14 |
| 150 | Taiwan | 47 | 1 | 17 | 29 | 95 | 13.57 |
| 151 | Chad | 46 | 0 | 15 | 31 | 38 | 5.43 |
| 152 | Macau | 45 | 0 | 28 | 17 | 95 | 13.57 |
| 153 | Reunion | 45 | 0 | 0 | 45 | 46 | 6.57 |
| 154 | Syria | 42 | 3 | 11 | 28 | 35 | 5.00 |
| 155 | Eritrea | 39 | 0 | 13 | 26 | 36 | 5.14 |
| 156 | Mongolia | 37 | 0 | 9 | 28 | 47 | 6.71 |
| 157 | Malawi | 33 | 3 | 4 | 26 | 24 | 3.43 |
| 158 | Martinique | 32 | 1 | 0 | 31 | 50 | 7.14 |
| 159 | Zimbabwe | 31 | 4 | 2 | 25 | 37 | 5.29 |
| 160 | occupied Palestinian territory | 25 | 0 | 0 | 25 | 47 | 6.71 |
| 161 | Angola | 25 | 2 | 6 | 17 | 37 | 5.29 |
| 162 | Antigua and Barbuda | 24 | 3 | 11 | 10 | 44 | 6.29 |
| 163 | Timor-Leste | 24 | 0 | 2 | 22 | 35 | 5.00 |
| 164 | Botswana | 22 | 1 | 0 | 21 | 27 | 3.86 |
| 165 | Palestine | 22 | 0 | 0 | 22 | 52 | 7.43 |
| 166 | Republic of Ireland | 21 | 0 | 0 | 21 | 49 | 7.00 |
| 167 | Laos | 19 | 0 | 7 | 12 | 33 | 4.71 |
| 168 | French Guiana | 18 | 0 | 6 | 12 | 50 | 7.14 |
| 169 | Grenada | 18 | 0 | 7 | 11 | 35 | 5.00 |
| 170 | Fiji | 18 | 0 | 10 | 8 | 38 | 5.43 |
| 171 | Belize | 18 | 2 | 5 | 11 | 34 | 4.86 |
| 172 | Namibia | 16 | 0 | 7 | 9 | 43 | 6.14 |
| 173 | Dominica | 16 | 0 | 13 | 3 | 35 | 5.00 |
| 174 | Central African Republic | 16 | 0 | 10 | 6 | 42 | 6.00 |
| 175 | Saint Lucia | 15 | 0 | 15 | 0 | 43 | 6.14 |
| 176 | Saint Kitts and Nevis | 15 | 0 | 2 | 13 | 32 | 4.57 |
| 177 | Saint Vincent and the Grenadines | 14 | 0 | 5 | 9 | 43 | 6.14 |
| 178 | Nicaragua | 12 | 3 | 7 | 2 | 38 | 5.43 |
| 179 | Burundi | 11 | 1 | 4 | 6 | 26 | 3.71 |
| 180 | Seychelles | 11 | 0 | 6 | 5 | 43 | 6.14 |
| 181 | Gambia | 10 | 1 | 8 | 1 | 35 | 5.00 |
| 182 | Suriname | 10 | 1 | 7 | 2 | 43 | 6.14 |
| 183 | Holy See | 9 | 0 | 2 | 7 | 47 | 6.71 |
| 184 | MS Zaandam | 9 | 2 | 0 | 7 | 29 | 4.14 |
| 185 | Israel | 8 | 0 | 0 | 8 | 65 | 9.29 |
| 186 | Papua New Guinea | 8 | 0 | 0 | 8 | 37 | 5.29 |
| 187 | Bhutan | 7 | 0 | 3 | 4 | 51 | 7.29 |
| 188 | Mauritania | 7 | 1 | 6 | 0 | 43 | 6.14 |
| 189 | Mayotte | 7 | 0 | 0 | 7 | 41 | 5.86 |
| 190 | Western Sahara | 6 | 0 | 5 | 1 | 21 | 3.00 |
| 191 | South Sudan | 5 | 0 | 0 | 5 | 21 | 3.00 |
| 192 | Germany | 5 | 0 | 0 | 5 | 89 | 12.71 |
| 193 | Bahamas, The | 4 | 0 | 0 | 4 | 38 | 5.43 |
| 194 | Sao Tome and Principe | 4 | 0 | 0 | 4 | 20 | 2.86 |
| 195 | Aruba | 4 | 0 | 0 | 4 | 44 | 6.29 |
| 196 | Puerto Rico | 3 | 0 | 0 | 3 | 41 | 5.86 |
| 197 | Guam | 3 | 0 | 0 | 3 | 41 | 5.86 |
| 198 | Saint Barthelemy | 3 | 0 | 0 | 3 | 53 | 7.57 |
| 199 | St. Martin | 2 | 0 | 0 | 2 | 48 | 6.86 |
| 200 | Faroe Islands | 2 | 0 | 0 | 2 | 53 | 7.57 |
| 201 | Lebanon | 2 | 0 | 0 | 2 | 65 | 9.29 |
| 202 | ('St. Martin',) | 2 | 0 | 0 | 2 | 47 | 6.71 |
| 203 | Jersey | 2 | 0 | 0 | 2 | 43 | 6.14 |
| 204 | Republic of the Congo | 1 | 0 | 0 | 1 | 41 | 5.86 |
| 205 | North Ireland | 1 | 0 | 0 | 1 | 58 | 8.29 |
| 206 | Yemen | 1 | 0 | 1 | 0 | 16 | 2.29 |
| 207 | Vatican City | 1 | 0 | 0 | 1 | 51 | 7.29 |
| 208 | Guernsey | 1 | 0 | 0 | 1 | 43 | 6.14 |
| 209 | The Bahamas | 1 | 0 | 0 | 1 | 41 | 5.86 |
| 210 | The Gambia | 1 | 0 | 0 | 1 | 40 | 5.71 |
| 211 | Cape Verde | 1 | 0 | 0 | 1 | 36 | 5.14 |
| 212 | Cayman Islands | 1 | 0 | 0 | 1 | 44 | 6.29 |
| 213 | Greenland | 1 | 0 | 0 | 1 | 41 | 5.86 |
| 214 | Channel Islands | 1 | 0 | 0 | 1 | 47 | 6.71 |
| 215 | Curacao | 1 | 0 | 0 | 1 | 43 | 6.14 |
| 216 | Gibraltar | 1 | 0 | 1 | 0 | 53 | 7.57 |
| 217 | East Timor | 1 | 0 | 0 | 1 | 36 | 5.14 |
| 218 | Gambia, The | 1 | 0 | 0 | 1 | 39 | 5.57 |
| 219 | Azerbaijan | 1 | 0 | 0 | 1 | 58 | 8.29 |
dfww_deaths = df5.sort_values(by='Deaths', ascending=False).reset_index(drop=True)
dfww_deaths.style.background_gradient(cmap='Reds').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
'Recovered': '{:.0f}', 'Active': '{:.0f}',
'Weeks Since 1st Case': '{:.2f}'})
| Country/Region | Confirmed | Deaths | Recovered | Active | Days Since 1st Case | Weeks Since 1st Case | |
|---|---|---|---|---|---|---|---|
| 0 | US | 939634 | 53786 | 101141 | 784707 | 95 | 13.57 |
| 1 | Italy | 195351 | 26384 | 63120 | 105847 | 86 | 12.29 |
| 2 | Spain | 223759 | 22902 | 95708 | 105149 | 85 | 12.14 |
| 3 | Belgium | 45325 | 6917 | 10417 | 27991 | 82 | 11.71 |
| 4 | Iran | 89328 | 5650 | 68193 | 15485 | 67 | 9.57 |
| 5 | Mainland China | 82827 | 4632 | 78225 | -30 | 95 | 13.57 |
| 6 | Brazil | 59324 | 4057 | 29160 | 26107 | 60 | 8.57 |
| 7 | Turkey | 107773 | 2706 | 25582 | 79485 | 46 | 6.57 |
| 8 | Canada | 46357 | 2565 | 14 | 43778 | 91 | 13.00 |
| 9 | Sweden | 18177 | 2192 | 1005 | 14980 | 86 | 12.29 |
| 10 | Switzerland | 28894 | 1599 | 21300 | 5995 | 61 | 8.71 |
| 11 | France | 35456 | 1456 | 2949 | 31051 | 93 | 13.29 |
| 12 | Mexico | 13842 | 1305 | 7149 | 5388 | 58 | 8.29 |
| 13 | Ireland | 18561 | 1063 | 9233 | 8265 | 57 | 8.14 |
| 14 | Portugal | 23392 | 880 | 1277 | 21235 | 55 | 7.86 |
| 15 | India | 26283 | 825 | 5939 | 19519 | 87 | 12.43 |
| 16 | Indonesia | 8607 | 720 | 1042 | 6845 | 55 | 7.86 |
| 17 | Peru | 25331 | 700 | 7797 | 16834 | 51 | 7.29 |
| 18 | Russia | 74588 | 681 | 6250 | 67657 | 86 | 12.29 |
| 19 | Romania | 10635 | 601 | 2890 | 7144 | 60 | 8.57 |
| 20 | Ecuador | 22719 | 576 | 1366 | 20777 | 56 | 8.00 |
| 21 | Austria | 15148 | 536 | 12103 | 2509 | 61 | 8.71 |
| 22 | Poland | 11273 | 524 | 2126 | 8623 | 53 | 7.57 |
| 23 | Philippines | 7294 | 494 | 792 | 6008 | 87 | 12.43 |
| 24 | Algeria | 3256 | 419 | 1479 | 1358 | 61 | 8.71 |
| 25 | Japan | 13231 | 360 | 1656 | 11215 | 95 | 13.57 |
| 26 | Egypt | 4319 | 307 | 1114 | 2898 | 72 | 10.29 |
| 27 | UK | 6668 | 303 | 857 | 5508 | 86 | 12.29 |
| 28 | Dominican Republic | 5926 | 273 | 822 | 4831 | 56 | 8.00 |
| 29 | Pakistan | 12723 | 269 | 2866 | 9588 | 60 | 8.57 |
| 30 | Hungary | 2443 | 262 | 458 | 1723 | 53 | 7.57 |
| 31 | South Korea | 10728 | 242 | 8717 | 1769 | 95 | 13.57 |
| 32 | Colombia | 5142 | 233 | 1067 | 3842 | 51 | 7.29 |
| 33 | Czech Republic | 7352 | 218 | 2453 | 4681 | 56 | 8.00 |
| 34 | Ukraine | 8125 | 201 | 782 | 7142 | 54 | 7.71 |
| 35 | Norway | 7499 | 201 | 32 | 7266 | 60 | 8.57 |
| 36 | Finland | 4475 | 186 | 2500 | 1789 | 88 | 12.57 |
| 37 | Argentina | 3780 | 185 | 1030 | 2565 | 54 | 7.71 |
| 38 | Chile | 12858 | 181 | 6746 | 5931 | 54 | 7.71 |
| 39 | Morocco | 3897 | 159 | 537 | 3201 | 55 | 7.86 |
| 40 | Panama | 5538 | 159 | 338 | 5041 | 47 | 6.71 |
| 41 | Netherlands | 3825 | 151 | 104 | 3570 | 59 | 8.43 |
| 42 | Bangladesh | 4998 | 140 | 113 | 4745 | 49 | 7.00 |
| 43 | Saudi Arabia | 16299 | 136 | 2215 | 13948 | 55 | 7.86 |
| 44 | Greece | 2506 | 130 | 577 | 1799 | 60 | 8.57 |
| 45 | Serbia | 6630 | 125 | 870 | 5635 | 51 | 7.29 |
| 46 | Malaysia | 5742 | 98 | 3762 | 1882 | 92 | 13.14 |
| 47 | Moldova | 3304 | 94 | 825 | 2385 | 49 | 7.00 |
| 48 | South Africa | 4361 | 86 | 1473 | 2802 | 52 | 7.43 |
| 49 | Iraq | 1763 | 86 | 1224 | 453 | 62 | 8.86 |
| 50 | Luxembourg | 3711 | 85 | 3088 | 538 | 57 | 8.14 |
| 51 | Slovenia | 1388 | 81 | 219 | 1088 | 52 | 7.43 |
| 52 | Australia | 6694 | 80 | 5271 | 1343 | 92 | 13.14 |
| 53 | United Arab Emirates | 9813 | 71 | 1887 | 7855 | 88 | 12.57 |
| 54 | Belarus | 9590 | 67 | 1573 | 7950 | 58 | 8.29 |
| 55 | North Macedonia | 1367 | 59 | 374 | 934 | 60 | 8.57 |
| 56 | Honduras | 627 | 59 | 65 | 503 | 46 | 6.57 |
| 57 | Bosnia and Herzegovina | 1486 | 57 | 592 | 837 | 52 | 7.43 |
| 58 | Bulgaria | 1247 | 55 | 197 | 995 | 49 | 7.00 |
| 59 | Croatia | 2016 | 54 | 1034 | 928 | 61 | 8.71 |
| 60 | Cameroon | 1518 | 53 | 697 | 768 | 51 | 7.29 |
| 61 | Thailand | 2907 | 51 | 2547 | 309 | 95 | 13.57 |
| 62 | Cuba | 1337 | 51 | 437 | 849 | 45 | 6.43 |
| 63 | Afghanistan | 1463 | 47 | 188 | 1228 | 62 | 8.86 |
| 64 | Bolivia | 866 | 46 | 54 | 766 | 46 | 6.57 |
| 65 | Estonia | 1635 | 46 | 228 | 1361 | 59 | 8.43 |
| 66 | Lithuania | 1426 | 41 | 460 | 925 | 58 | 8.29 |
| 67 | Burkina Faso | 629 | 41 | 442 | 146 | 47 | 6.71 |
| 68 | Andorra | 738 | 40 | 344 | 354 | 55 | 7.86 |
| 69 | San Marino | 513 | 40 | 64 | 409 | 59 | 8.43 |
| 70 | Tunisia | 939 | 38 | 207 | 694 | 53 | 7.57 |
| 71 | Nigeria | 1182 | 35 | 222 | 925 | 58 | 8.29 |
| 72 | Armenia | 1677 | 28 | 803 | 846 | 56 | 8.00 |
| 73 | Congo (Kinshasa) | 416 | 28 | 49 | 339 | 46 | 6.57 |
| 74 | Albania | 712 | 27 | 403 | 282 | 48 | 6.86 |
| 75 | Niger | 684 | 27 | 325 | 332 | 37 | 5.29 |
| 76 | Kazakhstan | 2601 | 25 | 646 | 1930 | 44 | 6.29 |
| 77 | Mali | 370 | 21 | 91 | 258 | 32 | 4.57 |
| 78 | Azerbaijan | 1617 | 21 | 1080 | 516 | 56 | 8.00 |
| 79 | Kuwait | 2892 | 19 | 656 | 2217 | 62 | 8.86 |
| 80 | New Zealand | 1470 | 18 | 1142 | 310 | 58 | 8.29 |
| 81 | Somalia | 390 | 18 | 8 | 364 | 41 | 5.86 |
| 82 | Slovakia | 1373 | 17 | 386 | 970 | 51 | 7.29 |
| 83 | Sudan | 213 | 17 | 19 | 177 | 44 | 6.29 |
| 84 | Cyprus | 810 | 14 | 148 | 648 | 48 | 6.86 |
| 85 | Uruguay | 596 | 14 | 370 | 212 | 43 | 6.14 |
| 86 | Kenya | 343 | 14 | 98 | 231 | 44 | 6.29 |
| 87 | Ivory Coast | 1077 | 14 | 419 | 644 | 90 | 12.86 |
| 88 | Denmark | 1524 | 13 | 190 | 1321 | 59 | 8.43 |
| 89 | Guatemala | 473 | 13 | 45 | 415 | 43 | 6.14 |
| 90 | Diamond Princess | 712 | 13 | 645 | 54 | 32 | 4.57 |
| 91 | Kosovo | 510 | 12 | 93 | 405 | 42 | 6.00 |
| 92 | Latvia | 804 | 12 | 267 | 525 | 55 | 7.86 |
| 93 | Singapore | 12693 | 12 | 1002 | 11679 | 94 | 13.43 |
| 94 | Liberia | 120 | 11 | 25 | 84 | 41 | 5.86 |
| 95 | Bahamas | 78 | 11 | 15 | 52 | 35 | 5.00 |
| 96 | Iceland | 1790 | 10 | 1570 | 210 | 58 | 8.29 |
| 97 | Ghana | 1279 | 10 | 134 | 1135 | 43 | 6.14 |
| 98 | Venezuela | 323 | 10 | 132 | 181 | 43 | 6.14 |
| 99 | Oman | 1905 | 10 | 329 | 1566 | 62 | 8.86 |
| 100 | Qatar | 9358 | 10 | 929 | 8419 | 57 | 8.14 |
| 101 | Tanzania | 299 | 10 | 48 | 241 | 41 | 5.86 |
| 102 | Mauritius | 331 | 9 | 295 | 27 | 39 | 5.57 |
| 103 | Paraguay | 228 | 9 | 85 | 134 | 49 | 7.00 |
| 104 | Kyrgyzstan | 665 | 8 | 345 | 312 | 39 | 5.57 |
| 105 | Uzbekistan | 1862 | 8 | 707 | 1147 | 42 | 6.00 |
| 106 | Bahrain | 2588 | 8 | 1160 | 1420 | 62 | 8.86 |
| 107 | El Salvador | 274 | 8 | 75 | 191 | 38 | 5.43 |
| 108 | Trinidad and Tobago | 115 | 8 | 53 | 54 | 43 | 6.14 |
| 109 | Senegal | 614 | 7 | 276 | 331 | 55 | 7.86 |
| 110 | Guyana | 73 | 7 | 12 | 54 | 45 | 6.43 |
| 111 | Guinea | 996 | 7 | 208 | 781 | 44 | 6.29 |
| 112 | Jamaica | 305 | 7 | 28 | 270 | 46 | 6.57 |
| 113 | Sri Lanka | 460 | 7 | 118 | 335 | 90 | 12.86 |
| 114 | Jordan | 444 | 7 | 332 | 105 | 54 | 7.71 |
| 115 | Haiti | 72 | 6 | 6 | 60 | 37 | 5.29 |
| 116 | Barbados | 79 | 6 | 31 | 42 | 40 | 5.71 |
| 117 | Togo | 96 | 6 | 62 | 28 | 51 | 7.29 |
| 118 | Congo (Brazzaville) | 200 | 6 | 19 | 175 | 42 | 6.00 |
| 119 | Costa Rica | 693 | 6 | 242 | 445 | 51 | 7.29 |
| 120 | Montenegro | 320 | 6 | 153 | 161 | 40 | 5.71 |
| 121 | Burma | 146 | 5 | 10 | 131 | 30 | 4.29 |
| 122 | Georgia | 456 | 5 | 139 | 312 | 60 | 8.57 |
| 123 | Zimbabwe | 31 | 4 | 2 | 25 | 37 | 5.29 |
| 124 | Malta | 448 | 4 | 249 | 195 | 50 | 7.14 |
| 125 | Hong Kong | 1037 | 4 | 753 | 280 | 94 | 13.43 |
| 126 | Monaco | 94 | 4 | 42 | 48 | 57 | 8.14 |
| 127 | West Bank and Gaza | 484 | 4 | 92 | 388 | 31 | 4.43 |
| 128 | Malawi | 33 | 3 | 4 | 26 | 24 | 3.43 |
| 129 | Zambia | 84 | 3 | 37 | 44 | 39 | 5.57 |
| 130 | Syria | 42 | 3 | 11 | 28 | 35 | 5.00 |
| 131 | Gabon | 176 | 3 | 30 | 143 | 43 | 6.14 |
| 132 | Ethiopia | 122 | 3 | 29 | 90 | 44 | 6.29 |
| 133 | Nicaragua | 12 | 3 | 7 | 2 | 38 | 5.43 |
| 134 | Antigua and Barbuda | 24 | 3 | 11 | 10 | 44 | 6.29 |
| 135 | Djibouti | 1008 | 2 | 373 | 633 | 39 | 5.57 |
| 136 | Belize | 18 | 2 | 5 | 11 | 34 | 4.86 |
| 137 | Sierra Leone | 82 | 2 | 10 | 70 | 26 | 3.71 |
| 138 | Libya | 61 | 2 | 18 | 41 | 33 | 4.71 |
| 139 | Angola | 25 | 2 | 6 | 17 | 37 | 5.29 |
| 140 | MS Zaandam | 9 | 2 | 0 | 7 | 29 | 4.14 |
| 141 | Liechtenstein | 81 | 1 | 55 | 25 | 53 | 7.57 |
| 142 | Equatorial Guinea | 258 | 1 | 7 | 250 | 42 | 6.00 |
| 143 | Taiwan | 47 | 1 | 17 | 29 | 95 | 13.57 |
| 144 | Suriname | 10 | 1 | 7 | 2 | 43 | 6.14 |
| 145 | Eswatini | 56 | 1 | 10 | 45 | 43 | 6.14 |
| 146 | Gambia | 10 | 1 | 8 | 1 | 35 | 5.00 |
| 147 | Cabo Verde | 90 | 1 | 1 | 88 | 37 | 5.29 |
| 148 | Benin | 54 | 1 | 27 | 26 | 41 | 5.86 |
| 149 | Burundi | 11 | 1 | 4 | 6 | 26 | 3.71 |
| 150 | Mauritania | 7 | 1 | 6 | 0 | 43 | 6.14 |
| 151 | Martinique | 32 | 1 | 0 | 31 | 50 | 7.14 |
| 152 | Brunei | 138 | 1 | 121 | 16 | 48 | 6.86 |
| 153 | Botswana | 22 | 1 | 0 | 21 | 27 | 3.86 |
| 154 | Sao Tome and Principe | 4 | 0 | 0 | 4 | 20 | 2.86 |
| 155 | Saint Vincent and the Grenadines | 14 | 0 | 5 | 9 | 43 | 6.14 |
| 156 | Uganda | 75 | 0 | 46 | 29 | 36 | 5.14 |
| 157 | Yemen | 1 | 0 | 1 | 0 | 16 | 2.29 |
| 158 | Seychelles | 11 | 0 | 6 | 5 | 43 | 6.14 |
| 159 | Western Sahara | 6 | 0 | 5 | 1 | 21 | 3.00 |
| 160 | Vietnam | 270 | 0 | 225 | 45 | 94 | 13.43 |
| 161 | Vatican City | 1 | 0 | 0 | 1 | 51 | 7.29 |
| 162 | South Sudan | 5 | 0 | 0 | 5 | 21 | 3.00 |
| 163 | Timor-Leste | 24 | 0 | 2 | 22 | 35 | 5.00 |
| 164 | The Gambia | 1 | 0 | 0 | 1 | 40 | 5.71 |
| 165 | The Bahamas | 1 | 0 | 0 | 1 | 41 | 5.86 |
| 166 | Saint Kitts and Nevis | 15 | 0 | 2 | 13 | 32 | 4.57 |
| 167 | St. Martin | 2 | 0 | 0 | 2 | 48 | 6.86 |
| 168 | Saint Lucia | 15 | 0 | 15 | 0 | 43 | 6.14 |
| 169 | Azerbaijan | 1 | 0 | 0 | 1 | 58 | 8.29 |
| 170 | Saint Barthelemy | 3 | 0 | 0 | 3 | 53 | 7.57 |
| 171 | East Timor | 1 | 0 | 0 | 1 | 36 | 5.14 |
| 172 | Guadeloupe | 53 | 0 | 0 | 53 | 44 | 6.29 |
| 173 | Grenada | 18 | 0 | 7 | 11 | 35 | 5.00 |
| 174 | Greenland | 1 | 0 | 0 | 1 | 41 | 5.86 |
| 175 | Gibraltar | 1 | 0 | 1 | 0 | 53 | 7.57 |
| 176 | Germany | 5 | 0 | 0 | 5 | 89 | 12.71 |
| 177 | Gambia, The | 1 | 0 | 0 | 1 | 39 | 5.57 |
| 178 | French Guiana | 18 | 0 | 6 | 12 | 50 | 7.14 |
| 179 | Fiji | 18 | 0 | 10 | 8 | 38 | 5.43 |
| 180 | Faroe Islands | 2 | 0 | 0 | 2 | 53 | 7.57 |
| 181 | Eritrea | 39 | 0 | 13 | 26 | 36 | 5.14 |
| 182 | Dominica | 16 | 0 | 13 | 3 | 35 | 5.00 |
| 183 | Rwanda | 183 | 0 | 88 | 95 | 43 | 6.14 |
| 184 | Curacao | 1 | 0 | 0 | 1 | 43 | 6.14 |
| 185 | Channel Islands | 1 | 0 | 0 | 1 | 47 | 6.71 |
| 186 | Chad | 46 | 0 | 15 | 31 | 38 | 5.43 |
| 187 | Central African Republic | 16 | 0 | 10 | 6 | 42 | 6.00 |
| 188 | Cayman Islands | 1 | 0 | 0 | 1 | 44 | 6.29 |
| 189 | Cape Verde | 1 | 0 | 0 | 1 | 36 | 5.14 |
| 190 | Cambodia | 122 | 0 | 117 | 5 | 90 | 12.86 |
| 191 | Bhutan | 7 | 0 | 3 | 4 | 51 | 7.29 |
| 192 | Bahamas, The | 4 | 0 | 0 | 4 | 38 | 5.43 |
| 193 | Aruba | 4 | 0 | 0 | 4 | 44 | 6.29 |
| 194 | Guam | 3 | 0 | 0 | 3 | 41 | 5.86 |
| 195 | Guernsey | 1 | 0 | 0 | 1 | 43 | 6.14 |
| 196 | Guinea-Bissau | 52 | 0 | 3 | 49 | 32 | 4.57 |
| 197 | Holy See | 9 | 0 | 2 | 7 | 47 | 6.71 |
| 198 | Reunion | 45 | 0 | 0 | 45 | 46 | 6.57 |
| 199 | Republic of the Congo | 1 | 0 | 0 | 1 | 41 | 5.86 |
| 200 | Republic of Ireland | 21 | 0 | 0 | 21 | 49 | 7.00 |
| 201 | Puerto Rico | 3 | 0 | 0 | 3 | 41 | 5.86 |
| 202 | Papua New Guinea | 8 | 0 | 0 | 8 | 37 | 5.29 |
| 203 | Palestine | 22 | 0 | 0 | 22 | 52 | 7.43 |
| 204 | Others | 61 | 0 | 0 | 61 | 79 | 11.29 |
| 205 | North Ireland | 1 | 0 | 0 | 1 | 58 | 8.29 |
| 206 | Nepal | 49 | 0 | 12 | 37 | 92 | 13.14 |
| 207 | Namibia | 16 | 0 | 7 | 9 | 43 | 6.14 |
| 208 | Mozambique | 70 | 0 | 12 | 58 | 35 | 5.00 |
| 209 | Mongolia | 37 | 0 | 9 | 28 | 47 | 6.71 |
| 210 | Mayotte | 7 | 0 | 0 | 7 | 41 | 5.86 |
| 211 | Maldives | 177 | 0 | 17 | 160 | 49 | 7.00 |
| 212 | Madagascar | 123 | 0 | 62 | 61 | 37 | 5.29 |
| 213 | Macau | 45 | 0 | 28 | 17 | 95 | 13.57 |
| 214 | Lebanon | 2 | 0 | 0 | 2 | 65 | 9.29 |
| 215 | Laos | 19 | 0 | 7 | 12 | 33 | 4.71 |
| 216 | ('St. Martin',) | 2 | 0 | 0 | 2 | 47 | 6.71 |
| 217 | Jersey | 2 | 0 | 0 | 2 | 43 | 6.14 |
| 218 | Israel | 8 | 0 | 0 | 8 | 65 | 9.29 |
| 219 | occupied Palestinian territory | 25 | 0 | 0 | 25 | 47 | 6.71 |
dfww_recovered = df5.sort_values(by='Recovered', ascending=False).reset_index(drop=True)
dfww_recovered.style.background_gradient(cmap='Greens').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
'Recovered': '{:.0f}', 'Active': '{:.0f}',
'Weeks Since 1st Case': '{:.2f}'})
| Country/Region | Confirmed | Deaths | Recovered | Active | Days Since 1st Case | Weeks Since 1st Case | |
|---|---|---|---|---|---|---|---|
| 0 | US | 939634 | 53786 | 101141 | 784707 | 95 | 13.57 |
| 1 | Spain | 223759 | 22902 | 95708 | 105149 | 85 | 12.14 |
| 2 | Mainland China | 82827 | 4632 | 78225 | -30 | 95 | 13.57 |
| 3 | Iran | 89328 | 5650 | 68193 | 15485 | 67 | 9.57 |
| 4 | Italy | 195351 | 26384 | 63120 | 105847 | 86 | 12.29 |
| 5 | Brazil | 59324 | 4057 | 29160 | 26107 | 60 | 8.57 |
| 6 | Turkey | 107773 | 2706 | 25582 | 79485 | 46 | 6.57 |
| 7 | Switzerland | 28894 | 1599 | 21300 | 5995 | 61 | 8.71 |
| 8 | Austria | 15148 | 536 | 12103 | 2509 | 61 | 8.71 |
| 9 | Belgium | 45325 | 6917 | 10417 | 27991 | 82 | 11.71 |
| 10 | Ireland | 18561 | 1063 | 9233 | 8265 | 57 | 8.14 |
| 11 | South Korea | 10728 | 242 | 8717 | 1769 | 95 | 13.57 |
| 12 | Peru | 25331 | 700 | 7797 | 16834 | 51 | 7.29 |
| 13 | Mexico | 13842 | 1305 | 7149 | 5388 | 58 | 8.29 |
| 14 | Chile | 12858 | 181 | 6746 | 5931 | 54 | 7.71 |
| 15 | Russia | 74588 | 681 | 6250 | 67657 | 86 | 12.29 |
| 16 | India | 26283 | 825 | 5939 | 19519 | 87 | 12.43 |
| 17 | Australia | 6694 | 80 | 5271 | 1343 | 92 | 13.14 |
| 18 | Malaysia | 5742 | 98 | 3762 | 1882 | 92 | 13.14 |
| 19 | Luxembourg | 3711 | 85 | 3088 | 538 | 57 | 8.14 |
| 20 | France | 35456 | 1456 | 2949 | 31051 | 93 | 13.29 |
| 21 | Romania | 10635 | 601 | 2890 | 7144 | 60 | 8.57 |
| 22 | Pakistan | 12723 | 269 | 2866 | 9588 | 60 | 8.57 |
| 23 | Thailand | 2907 | 51 | 2547 | 309 | 95 | 13.57 |
| 24 | Finland | 4475 | 186 | 2500 | 1789 | 88 | 12.57 |
| 25 | Czech Republic | 7352 | 218 | 2453 | 4681 | 56 | 8.00 |
| 26 | Saudi Arabia | 16299 | 136 | 2215 | 13948 | 55 | 7.86 |
| 27 | Poland | 11273 | 524 | 2126 | 8623 | 53 | 7.57 |
| 28 | United Arab Emirates | 9813 | 71 | 1887 | 7855 | 88 | 12.57 |
| 29 | Japan | 13231 | 360 | 1656 | 11215 | 95 | 13.57 |
| 30 | Belarus | 9590 | 67 | 1573 | 7950 | 58 | 8.29 |
| 31 | Iceland | 1790 | 10 | 1570 | 210 | 58 | 8.29 |
| 32 | Algeria | 3256 | 419 | 1479 | 1358 | 61 | 8.71 |
| 33 | South Africa | 4361 | 86 | 1473 | 2802 | 52 | 7.43 |
| 34 | Ecuador | 22719 | 576 | 1366 | 20777 | 56 | 8.00 |
| 35 | Portugal | 23392 | 880 | 1277 | 21235 | 55 | 7.86 |
| 36 | Iraq | 1763 | 86 | 1224 | 453 | 62 | 8.86 |
| 37 | Bahrain | 2588 | 8 | 1160 | 1420 | 62 | 8.86 |
| 38 | New Zealand | 1470 | 18 | 1142 | 310 | 58 | 8.29 |
| 39 | Egypt | 4319 | 307 | 1114 | 2898 | 72 | 10.29 |
| 40 | Azerbaijan | 1617 | 21 | 1080 | 516 | 56 | 8.00 |
| 41 | Colombia | 5142 | 233 | 1067 | 3842 | 51 | 7.29 |
| 42 | Indonesia | 8607 | 720 | 1042 | 6845 | 55 | 7.86 |
| 43 | Croatia | 2016 | 54 | 1034 | 928 | 61 | 8.71 |
| 44 | Argentina | 3780 | 185 | 1030 | 2565 | 54 | 7.71 |
| 45 | Sweden | 18177 | 2192 | 1005 | 14980 | 86 | 12.29 |
| 46 | Singapore | 12693 | 12 | 1002 | 11679 | 94 | 13.43 |
| 47 | Qatar | 9358 | 10 | 929 | 8419 | 57 | 8.14 |
| 48 | Serbia | 6630 | 125 | 870 | 5635 | 51 | 7.29 |
| 49 | UK | 6668 | 303 | 857 | 5508 | 86 | 12.29 |
| 50 | Moldova | 3304 | 94 | 825 | 2385 | 49 | 7.00 |
| 51 | Dominican Republic | 5926 | 273 | 822 | 4831 | 56 | 8.00 |
| 52 | Armenia | 1677 | 28 | 803 | 846 | 56 | 8.00 |
| 53 | Philippines | 7294 | 494 | 792 | 6008 | 87 | 12.43 |
| 54 | Ukraine | 8125 | 201 | 782 | 7142 | 54 | 7.71 |
| 55 | Hong Kong | 1037 | 4 | 753 | 280 | 94 | 13.43 |
| 56 | Uzbekistan | 1862 | 8 | 707 | 1147 | 42 | 6.00 |
| 57 | Cameroon | 1518 | 53 | 697 | 768 | 51 | 7.29 |
| 58 | Kuwait | 2892 | 19 | 656 | 2217 | 62 | 8.86 |
| 59 | Kazakhstan | 2601 | 25 | 646 | 1930 | 44 | 6.29 |
| 60 | Diamond Princess | 712 | 13 | 645 | 54 | 32 | 4.57 |
| 61 | Bosnia and Herzegovina | 1486 | 57 | 592 | 837 | 52 | 7.43 |
| 62 | Greece | 2506 | 130 | 577 | 1799 | 60 | 8.57 |
| 63 | Morocco | 3897 | 159 | 537 | 3201 | 55 | 7.86 |
| 64 | Lithuania | 1426 | 41 | 460 | 925 | 58 | 8.29 |
| 65 | Hungary | 2443 | 262 | 458 | 1723 | 53 | 7.57 |
| 66 | Burkina Faso | 629 | 41 | 442 | 146 | 47 | 6.71 |
| 67 | Cuba | 1337 | 51 | 437 | 849 | 45 | 6.43 |
| 68 | Ivory Coast | 1077 | 14 | 419 | 644 | 90 | 12.86 |
| 69 | Albania | 712 | 27 | 403 | 282 | 48 | 6.86 |
| 70 | Slovakia | 1373 | 17 | 386 | 970 | 51 | 7.29 |
| 71 | North Macedonia | 1367 | 59 | 374 | 934 | 60 | 8.57 |
| 72 | Djibouti | 1008 | 2 | 373 | 633 | 39 | 5.57 |
| 73 | Uruguay | 596 | 14 | 370 | 212 | 43 | 6.14 |
| 74 | Kyrgyzstan | 665 | 8 | 345 | 312 | 39 | 5.57 |
| 75 | Andorra | 738 | 40 | 344 | 354 | 55 | 7.86 |
| 76 | Panama | 5538 | 159 | 338 | 5041 | 47 | 6.71 |
| 77 | Jordan | 444 | 7 | 332 | 105 | 54 | 7.71 |
| 78 | Oman | 1905 | 10 | 329 | 1566 | 62 | 8.86 |
| 79 | Niger | 684 | 27 | 325 | 332 | 37 | 5.29 |
| 80 | Mauritius | 331 | 9 | 295 | 27 | 39 | 5.57 |
| 81 | Senegal | 614 | 7 | 276 | 331 | 55 | 7.86 |
| 82 | Latvia | 804 | 12 | 267 | 525 | 55 | 7.86 |
| 83 | Malta | 448 | 4 | 249 | 195 | 50 | 7.14 |
| 84 | Costa Rica | 693 | 6 | 242 | 445 | 51 | 7.29 |
| 85 | Estonia | 1635 | 46 | 228 | 1361 | 59 | 8.43 |
| 86 | Vietnam | 270 | 0 | 225 | 45 | 94 | 13.43 |
| 87 | Nigeria | 1182 | 35 | 222 | 925 | 58 | 8.29 |
| 88 | Slovenia | 1388 | 81 | 219 | 1088 | 52 | 7.43 |
| 89 | Guinea | 996 | 7 | 208 | 781 | 44 | 6.29 |
| 90 | Tunisia | 939 | 38 | 207 | 694 | 53 | 7.57 |
| 91 | Bulgaria | 1247 | 55 | 197 | 995 | 49 | 7.00 |
| 92 | Denmark | 1524 | 13 | 190 | 1321 | 59 | 8.43 |
| 93 | Afghanistan | 1463 | 47 | 188 | 1228 | 62 | 8.86 |
| 94 | Montenegro | 320 | 6 | 153 | 161 | 40 | 5.71 |
| 95 | Cyprus | 810 | 14 | 148 | 648 | 48 | 6.86 |
| 96 | Georgia | 456 | 5 | 139 | 312 | 60 | 8.57 |
| 97 | Ghana | 1279 | 10 | 134 | 1135 | 43 | 6.14 |
| 98 | Venezuela | 323 | 10 | 132 | 181 | 43 | 6.14 |
| 99 | Brunei | 138 | 1 | 121 | 16 | 48 | 6.86 |
| 100 | Sri Lanka | 460 | 7 | 118 | 335 | 90 | 12.86 |
| 101 | Cambodia | 122 | 0 | 117 | 5 | 90 | 12.86 |
| 102 | Bangladesh | 4998 | 140 | 113 | 4745 | 49 | 7.00 |
| 103 | Netherlands | 3825 | 151 | 104 | 3570 | 59 | 8.43 |
| 104 | Kenya | 343 | 14 | 98 | 231 | 44 | 6.29 |
| 105 | Kosovo | 510 | 12 | 93 | 405 | 42 | 6.00 |
| 106 | West Bank and Gaza | 484 | 4 | 92 | 388 | 31 | 4.43 |
| 107 | Mali | 370 | 21 | 91 | 258 | 32 | 4.57 |
| 108 | Rwanda | 183 | 0 | 88 | 95 | 43 | 6.14 |
| 109 | Paraguay | 228 | 9 | 85 | 134 | 49 | 7.00 |
| 110 | El Salvador | 274 | 8 | 75 | 191 | 38 | 5.43 |
| 111 | Honduras | 627 | 59 | 65 | 503 | 46 | 6.57 |
| 112 | San Marino | 513 | 40 | 64 | 409 | 59 | 8.43 |
| 113 | Madagascar | 123 | 0 | 62 | 61 | 37 | 5.29 |
| 114 | Togo | 96 | 6 | 62 | 28 | 51 | 7.29 |
| 115 | Liechtenstein | 81 | 1 | 55 | 25 | 53 | 7.57 |
| 116 | Bolivia | 866 | 46 | 54 | 766 | 46 | 6.57 |
| 117 | Trinidad and Tobago | 115 | 8 | 53 | 54 | 43 | 6.14 |
| 118 | Congo (Kinshasa) | 416 | 28 | 49 | 339 | 46 | 6.57 |
| 119 | Tanzania | 299 | 10 | 48 | 241 | 41 | 5.86 |
| 120 | Uganda | 75 | 0 | 46 | 29 | 36 | 5.14 |
| 121 | Guatemala | 473 | 13 | 45 | 415 | 43 | 6.14 |
| 122 | Monaco | 94 | 4 | 42 | 48 | 57 | 8.14 |
| 123 | Zambia | 84 | 3 | 37 | 44 | 39 | 5.57 |
| 124 | Norway | 7499 | 201 | 32 | 7266 | 60 | 8.57 |
| 125 | Barbados | 79 | 6 | 31 | 42 | 40 | 5.71 |
| 126 | Gabon | 176 | 3 | 30 | 143 | 43 | 6.14 |
| 127 | Ethiopia | 122 | 3 | 29 | 90 | 44 | 6.29 |
| 128 | Jamaica | 305 | 7 | 28 | 270 | 46 | 6.57 |
| 129 | Macau | 45 | 0 | 28 | 17 | 95 | 13.57 |
| 130 | Benin | 54 | 1 | 27 | 26 | 41 | 5.86 |
| 131 | Liberia | 120 | 11 | 25 | 84 | 41 | 5.86 |
| 132 | Sudan | 213 | 17 | 19 | 177 | 44 | 6.29 |
| 133 | Congo (Brazzaville) | 200 | 6 | 19 | 175 | 42 | 6.00 |
| 134 | Libya | 61 | 2 | 18 | 41 | 33 | 4.71 |
| 135 | Taiwan | 47 | 1 | 17 | 29 | 95 | 13.57 |
| 136 | Maldives | 177 | 0 | 17 | 160 | 49 | 7.00 |
| 137 | Saint Lucia | 15 | 0 | 15 | 0 | 43 | 6.14 |
| 138 | Bahamas | 78 | 11 | 15 | 52 | 35 | 5.00 |
| 139 | Chad | 46 | 0 | 15 | 31 | 38 | 5.43 |
| 140 | Canada | 46357 | 2565 | 14 | 43778 | 91 | 13.00 |
| 141 | Dominica | 16 | 0 | 13 | 3 | 35 | 5.00 |
| 142 | Eritrea | 39 | 0 | 13 | 26 | 36 | 5.14 |
| 143 | Nepal | 49 | 0 | 12 | 37 | 92 | 13.14 |
| 144 | Mozambique | 70 | 0 | 12 | 58 | 35 | 5.00 |
| 145 | Guyana | 73 | 7 | 12 | 54 | 45 | 6.43 |
| 146 | Syria | 42 | 3 | 11 | 28 | 35 | 5.00 |
| 147 | Antigua and Barbuda | 24 | 3 | 11 | 10 | 44 | 6.29 |
| 148 | Eswatini | 56 | 1 | 10 | 45 | 43 | 6.14 |
| 149 | Fiji | 18 | 0 | 10 | 8 | 38 | 5.43 |
| 150 | Central African Republic | 16 | 0 | 10 | 6 | 42 | 6.00 |
| 151 | Sierra Leone | 82 | 2 | 10 | 70 | 26 | 3.71 |
| 152 | Burma | 146 | 5 | 10 | 131 | 30 | 4.29 |
| 153 | Mongolia | 37 | 0 | 9 | 28 | 47 | 6.71 |
| 154 | Somalia | 390 | 18 | 8 | 364 | 41 | 5.86 |
| 155 | Gambia | 10 | 1 | 8 | 1 | 35 | 5.00 |
| 156 | Suriname | 10 | 1 | 7 | 2 | 43 | 6.14 |
| 157 | Grenada | 18 | 0 | 7 | 11 | 35 | 5.00 |
| 158 | Namibia | 16 | 0 | 7 | 9 | 43 | 6.14 |
| 159 | Laos | 19 | 0 | 7 | 12 | 33 | 4.71 |
| 160 | Equatorial Guinea | 258 | 1 | 7 | 250 | 42 | 6.00 |
| 161 | Nicaragua | 12 | 3 | 7 | 2 | 38 | 5.43 |
| 162 | French Guiana | 18 | 0 | 6 | 12 | 50 | 7.14 |
| 163 | Haiti | 72 | 6 | 6 | 60 | 37 | 5.29 |
| 164 | Angola | 25 | 2 | 6 | 17 | 37 | 5.29 |
| 165 | Mauritania | 7 | 1 | 6 | 0 | 43 | 6.14 |
| 166 | Seychelles | 11 | 0 | 6 | 5 | 43 | 6.14 |
| 167 | Saint Vincent and the Grenadines | 14 | 0 | 5 | 9 | 43 | 6.14 |
| 168 | Western Sahara | 6 | 0 | 5 | 1 | 21 | 3.00 |
| 169 | Belize | 18 | 2 | 5 | 11 | 34 | 4.86 |
| 170 | Malawi | 33 | 3 | 4 | 26 | 24 | 3.43 |
| 171 | Burundi | 11 | 1 | 4 | 6 | 26 | 3.71 |
| 172 | Bhutan | 7 | 0 | 3 | 4 | 51 | 7.29 |
| 173 | Guinea-Bissau | 52 | 0 | 3 | 49 | 32 | 4.57 |
| 174 | Saint Kitts and Nevis | 15 | 0 | 2 | 13 | 32 | 4.57 |
| 175 | Zimbabwe | 31 | 4 | 2 | 25 | 37 | 5.29 |
| 176 | Holy See | 9 | 0 | 2 | 7 | 47 | 6.71 |
| 177 | Timor-Leste | 24 | 0 | 2 | 22 | 35 | 5.00 |
| 178 | Gibraltar | 1 | 0 | 1 | 0 | 53 | 7.57 |
| 179 | Yemen | 1 | 0 | 1 | 0 | 16 | 2.29 |
| 180 | Cabo Verde | 90 | 1 | 1 | 88 | 37 | 5.29 |
| 181 | Vatican City | 1 | 0 | 0 | 1 | 51 | 7.29 |
| 182 | St. Martin | 2 | 0 | 0 | 2 | 48 | 6.86 |
| 183 | The Bahamas | 1 | 0 | 0 | 1 | 41 | 5.86 |
| 184 | The Gambia | 1 | 0 | 0 | 1 | 40 | 5.71 |
| 185 | Azerbaijan | 1 | 0 | 0 | 1 | 58 | 8.29 |
| 186 | South Sudan | 5 | 0 | 0 | 5 | 21 | 3.00 |
| 187 | East Timor | 1 | 0 | 0 | 1 | 36 | 5.14 |
| 188 | Guam | 3 | 0 | 0 | 3 | 41 | 5.86 |
| 189 | Guadeloupe | 53 | 0 | 0 | 53 | 44 | 6.29 |
| 190 | Greenland | 1 | 0 | 0 | 1 | 41 | 5.86 |
| 191 | Germany | 5 | 0 | 0 | 5 | 89 | 12.71 |
| 192 | Gambia, The | 1 | 0 | 0 | 1 | 39 | 5.57 |
| 193 | Faroe Islands | 2 | 0 | 0 | 2 | 53 | 7.57 |
| 194 | Curacao | 1 | 0 | 0 | 1 | 43 | 6.14 |
| 195 | Sao Tome and Principe | 4 | 0 | 0 | 4 | 20 | 2.86 |
| 196 | Channel Islands | 1 | 0 | 0 | 1 | 47 | 6.71 |
| 197 | Cayman Islands | 1 | 0 | 0 | 1 | 44 | 6.29 |
| 198 | Cape Verde | 1 | 0 | 0 | 1 | 36 | 5.14 |
| 199 | Botswana | 22 | 1 | 0 | 21 | 27 | 3.86 |
| 200 | Bahamas, The | 4 | 0 | 0 | 4 | 38 | 5.43 |
| 201 | Aruba | 4 | 0 | 0 | 4 | 44 | 6.29 |
| 202 | Guernsey | 1 | 0 | 0 | 1 | 43 | 6.14 |
| 203 | Israel | 8 | 0 | 0 | 8 | 65 | 9.29 |
| 204 | Jersey | 2 | 0 | 0 | 2 | 43 | 6.14 |
| 205 | ('St. Martin',) | 2 | 0 | 0 | 2 | 47 | 6.71 |
| 206 | Lebanon | 2 | 0 | 0 | 2 | 65 | 9.29 |
| 207 | MS Zaandam | 9 | 2 | 0 | 7 | 29 | 4.14 |
| 208 | Martinique | 32 | 1 | 0 | 31 | 50 | 7.14 |
| 209 | Mayotte | 7 | 0 | 0 | 7 | 41 | 5.86 |
| 210 | North Ireland | 1 | 0 | 0 | 1 | 58 | 8.29 |
| 211 | Others | 61 | 0 | 0 | 61 | 79 | 11.29 |
| 212 | Palestine | 22 | 0 | 0 | 22 | 52 | 7.43 |
| 213 | Papua New Guinea | 8 | 0 | 0 | 8 | 37 | 5.29 |
| 214 | Puerto Rico | 3 | 0 | 0 | 3 | 41 | 5.86 |
| 215 | Republic of Ireland | 21 | 0 | 0 | 21 | 49 | 7.00 |
| 216 | Republic of the Congo | 1 | 0 | 0 | 1 | 41 | 5.86 |
| 217 | Reunion | 45 | 0 | 0 | 45 | 46 | 6.57 |
| 218 | Saint Barthelemy | 3 | 0 | 0 | 3 | 53 | 7.57 |
| 219 | occupied Palestinian territory | 25 | 0 | 0 | 25 | 47 | 6.71 |
dfww_active = df5.sort_values(by='Active', ascending=False).reset_index(drop=True)
dfww_active.style.background_gradient(cmap='YlOrRd').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
'Recovered': '{:.0f}', 'Active': '{:.0f}',
'Weeks Since 1st Case': '{:.2f}'})
| Country/Region | Confirmed | Deaths | Recovered | Active | Days Since 1st Case | Weeks Since 1st Case | |
|---|---|---|---|---|---|---|---|
| 0 | US | 939634 | 53786 | 101141 | 784707 | 95 | 13.57 |
| 1 | Italy | 195351 | 26384 | 63120 | 105847 | 86 | 12.29 |
| 2 | Spain | 223759 | 22902 | 95708 | 105149 | 85 | 12.14 |
| 3 | Turkey | 107773 | 2706 | 25582 | 79485 | 46 | 6.57 |
| 4 | Russia | 74588 | 681 | 6250 | 67657 | 86 | 12.29 |
| 5 | Canada | 46357 | 2565 | 14 | 43778 | 91 | 13.00 |
| 6 | France | 35456 | 1456 | 2949 | 31051 | 93 | 13.29 |
| 7 | Belgium | 45325 | 6917 | 10417 | 27991 | 82 | 11.71 |
| 8 | Brazil | 59324 | 4057 | 29160 | 26107 | 60 | 8.57 |
| 9 | Portugal | 23392 | 880 | 1277 | 21235 | 55 | 7.86 |
| 10 | Ecuador | 22719 | 576 | 1366 | 20777 | 56 | 8.00 |
| 11 | India | 26283 | 825 | 5939 | 19519 | 87 | 12.43 |
| 12 | Peru | 25331 | 700 | 7797 | 16834 | 51 | 7.29 |
| 13 | Iran | 89328 | 5650 | 68193 | 15485 | 67 | 9.57 |
| 14 | Sweden | 18177 | 2192 | 1005 | 14980 | 86 | 12.29 |
| 15 | Saudi Arabia | 16299 | 136 | 2215 | 13948 | 55 | 7.86 |
| 16 | Singapore | 12693 | 12 | 1002 | 11679 | 94 | 13.43 |
| 17 | Japan | 13231 | 360 | 1656 | 11215 | 95 | 13.57 |
| 18 | Pakistan | 12723 | 269 | 2866 | 9588 | 60 | 8.57 |
| 19 | Poland | 11273 | 524 | 2126 | 8623 | 53 | 7.57 |
| 20 | Qatar | 9358 | 10 | 929 | 8419 | 57 | 8.14 |
| 21 | Ireland | 18561 | 1063 | 9233 | 8265 | 57 | 8.14 |
| 22 | Belarus | 9590 | 67 | 1573 | 7950 | 58 | 8.29 |
| 23 | United Arab Emirates | 9813 | 71 | 1887 | 7855 | 88 | 12.57 |
| 24 | Norway | 7499 | 201 | 32 | 7266 | 60 | 8.57 |
| 25 | Romania | 10635 | 601 | 2890 | 7144 | 60 | 8.57 |
| 26 | Ukraine | 8125 | 201 | 782 | 7142 | 54 | 7.71 |
| 27 | Indonesia | 8607 | 720 | 1042 | 6845 | 55 | 7.86 |
| 28 | Philippines | 7294 | 494 | 792 | 6008 | 87 | 12.43 |
| 29 | Switzerland | 28894 | 1599 | 21300 | 5995 | 61 | 8.71 |
| 30 | Chile | 12858 | 181 | 6746 | 5931 | 54 | 7.71 |
| 31 | Serbia | 6630 | 125 | 870 | 5635 | 51 | 7.29 |
| 32 | UK | 6668 | 303 | 857 | 5508 | 86 | 12.29 |
| 33 | Mexico | 13842 | 1305 | 7149 | 5388 | 58 | 8.29 |
| 34 | Panama | 5538 | 159 | 338 | 5041 | 47 | 6.71 |
| 35 | Dominican Republic | 5926 | 273 | 822 | 4831 | 56 | 8.00 |
| 36 | Bangladesh | 4998 | 140 | 113 | 4745 | 49 | 7.00 |
| 37 | Czech Republic | 7352 | 218 | 2453 | 4681 | 56 | 8.00 |
| 38 | Colombia | 5142 | 233 | 1067 | 3842 | 51 | 7.29 |
| 39 | Netherlands | 3825 | 151 | 104 | 3570 | 59 | 8.43 |
| 40 | Morocco | 3897 | 159 | 537 | 3201 | 55 | 7.86 |
| 41 | Egypt | 4319 | 307 | 1114 | 2898 | 72 | 10.29 |
| 42 | South Africa | 4361 | 86 | 1473 | 2802 | 52 | 7.43 |
| 43 | Argentina | 3780 | 185 | 1030 | 2565 | 54 | 7.71 |
| 44 | Austria | 15148 | 536 | 12103 | 2509 | 61 | 8.71 |
| 45 | Moldova | 3304 | 94 | 825 | 2385 | 49 | 7.00 |
| 46 | Kuwait | 2892 | 19 | 656 | 2217 | 62 | 8.86 |
| 47 | Kazakhstan | 2601 | 25 | 646 | 1930 | 44 | 6.29 |
| 48 | Malaysia | 5742 | 98 | 3762 | 1882 | 92 | 13.14 |
| 49 | Greece | 2506 | 130 | 577 | 1799 | 60 | 8.57 |
| 50 | Finland | 4475 | 186 | 2500 | 1789 | 88 | 12.57 |
| 51 | South Korea | 10728 | 242 | 8717 | 1769 | 95 | 13.57 |
| 52 | Hungary | 2443 | 262 | 458 | 1723 | 53 | 7.57 |
| 53 | Oman | 1905 | 10 | 329 | 1566 | 62 | 8.86 |
| 54 | Bahrain | 2588 | 8 | 1160 | 1420 | 62 | 8.86 |
| 55 | Estonia | 1635 | 46 | 228 | 1361 | 59 | 8.43 |
| 56 | Algeria | 3256 | 419 | 1479 | 1358 | 61 | 8.71 |
| 57 | Australia | 6694 | 80 | 5271 | 1343 | 92 | 13.14 |
| 58 | Denmark | 1524 | 13 | 190 | 1321 | 59 | 8.43 |
| 59 | Afghanistan | 1463 | 47 | 188 | 1228 | 62 | 8.86 |
| 60 | Uzbekistan | 1862 | 8 | 707 | 1147 | 42 | 6.00 |
| 61 | Ghana | 1279 | 10 | 134 | 1135 | 43 | 6.14 |
| 62 | Slovenia | 1388 | 81 | 219 | 1088 | 52 | 7.43 |
| 63 | Bulgaria | 1247 | 55 | 197 | 995 | 49 | 7.00 |
| 64 | Slovakia | 1373 | 17 | 386 | 970 | 51 | 7.29 |
| 65 | North Macedonia | 1367 | 59 | 374 | 934 | 60 | 8.57 |
| 66 | Croatia | 2016 | 54 | 1034 | 928 | 61 | 8.71 |
| 67 | Lithuania | 1426 | 41 | 460 | 925 | 58 | 8.29 |
| 68 | Nigeria | 1182 | 35 | 222 | 925 | 58 | 8.29 |
| 69 | Cuba | 1337 | 51 | 437 | 849 | 45 | 6.43 |
| 70 | Armenia | 1677 | 28 | 803 | 846 | 56 | 8.00 |
| 71 | Bosnia and Herzegovina | 1486 | 57 | 592 | 837 | 52 | 7.43 |
| 72 | Guinea | 996 | 7 | 208 | 781 | 44 | 6.29 |
| 73 | Cameroon | 1518 | 53 | 697 | 768 | 51 | 7.29 |
| 74 | Bolivia | 866 | 46 | 54 | 766 | 46 | 6.57 |
| 75 | Tunisia | 939 | 38 | 207 | 694 | 53 | 7.57 |
| 76 | Cyprus | 810 | 14 | 148 | 648 | 48 | 6.86 |
| 77 | Ivory Coast | 1077 | 14 | 419 | 644 | 90 | 12.86 |
| 78 | Djibouti | 1008 | 2 | 373 | 633 | 39 | 5.57 |
| 79 | Luxembourg | 3711 | 85 | 3088 | 538 | 57 | 8.14 |
| 80 | Latvia | 804 | 12 | 267 | 525 | 55 | 7.86 |
| 81 | Azerbaijan | 1617 | 21 | 1080 | 516 | 56 | 8.00 |
| 82 | Honduras | 627 | 59 | 65 | 503 | 46 | 6.57 |
| 83 | Iraq | 1763 | 86 | 1224 | 453 | 62 | 8.86 |
| 84 | Costa Rica | 693 | 6 | 242 | 445 | 51 | 7.29 |
| 85 | Guatemala | 473 | 13 | 45 | 415 | 43 | 6.14 |
| 86 | San Marino | 513 | 40 | 64 | 409 | 59 | 8.43 |
| 87 | Kosovo | 510 | 12 | 93 | 405 | 42 | 6.00 |
| 88 | West Bank and Gaza | 484 | 4 | 92 | 388 | 31 | 4.43 |
| 89 | Somalia | 390 | 18 | 8 | 364 | 41 | 5.86 |
| 90 | Andorra | 738 | 40 | 344 | 354 | 55 | 7.86 |
| 91 | Congo (Kinshasa) | 416 | 28 | 49 | 339 | 46 | 6.57 |
| 92 | Sri Lanka | 460 | 7 | 118 | 335 | 90 | 12.86 |
| 93 | Niger | 684 | 27 | 325 | 332 | 37 | 5.29 |
| 94 | Senegal | 614 | 7 | 276 | 331 | 55 | 7.86 |
| 95 | Kyrgyzstan | 665 | 8 | 345 | 312 | 39 | 5.57 |
| 96 | Georgia | 456 | 5 | 139 | 312 | 60 | 8.57 |
| 97 | New Zealand | 1470 | 18 | 1142 | 310 | 58 | 8.29 |
| 98 | Thailand | 2907 | 51 | 2547 | 309 | 95 | 13.57 |
| 99 | Albania | 712 | 27 | 403 | 282 | 48 | 6.86 |
| 100 | Hong Kong | 1037 | 4 | 753 | 280 | 94 | 13.43 |
| 101 | Jamaica | 305 | 7 | 28 | 270 | 46 | 6.57 |
| 102 | Mali | 370 | 21 | 91 | 258 | 32 | 4.57 |
| 103 | Equatorial Guinea | 258 | 1 | 7 | 250 | 42 | 6.00 |
| 104 | Tanzania | 299 | 10 | 48 | 241 | 41 | 5.86 |
| 105 | Kenya | 343 | 14 | 98 | 231 | 44 | 6.29 |
| 106 | Uruguay | 596 | 14 | 370 | 212 | 43 | 6.14 |
| 107 | Iceland | 1790 | 10 | 1570 | 210 | 58 | 8.29 |
| 108 | Malta | 448 | 4 | 249 | 195 | 50 | 7.14 |
| 109 | El Salvador | 274 | 8 | 75 | 191 | 38 | 5.43 |
| 110 | Venezuela | 323 | 10 | 132 | 181 | 43 | 6.14 |
| 111 | Sudan | 213 | 17 | 19 | 177 | 44 | 6.29 |
| 112 | Congo (Brazzaville) | 200 | 6 | 19 | 175 | 42 | 6.00 |
| 113 | Montenegro | 320 | 6 | 153 | 161 | 40 | 5.71 |
| 114 | Maldives | 177 | 0 | 17 | 160 | 49 | 7.00 |
| 115 | Burkina Faso | 629 | 41 | 442 | 146 | 47 | 6.71 |
| 116 | Gabon | 176 | 3 | 30 | 143 | 43 | 6.14 |
| 117 | Paraguay | 228 | 9 | 85 | 134 | 49 | 7.00 |
| 118 | Burma | 146 | 5 | 10 | 131 | 30 | 4.29 |
| 119 | Jordan | 444 | 7 | 332 | 105 | 54 | 7.71 |
| 120 | Rwanda | 183 | 0 | 88 | 95 | 43 | 6.14 |
| 121 | Ethiopia | 122 | 3 | 29 | 90 | 44 | 6.29 |
| 122 | Cabo Verde | 90 | 1 | 1 | 88 | 37 | 5.29 |
| 123 | Liberia | 120 | 11 | 25 | 84 | 41 | 5.86 |
| 124 | Sierra Leone | 82 | 2 | 10 | 70 | 26 | 3.71 |
| 125 | Madagascar | 123 | 0 | 62 | 61 | 37 | 5.29 |
| 126 | Others | 61 | 0 | 0 | 61 | 79 | 11.29 |
| 127 | Haiti | 72 | 6 | 6 | 60 | 37 | 5.29 |
| 128 | Mozambique | 70 | 0 | 12 | 58 | 35 | 5.00 |
| 129 | Trinidad and Tobago | 115 | 8 | 53 | 54 | 43 | 6.14 |
| 130 | Guyana | 73 | 7 | 12 | 54 | 45 | 6.43 |
| 131 | Diamond Princess | 712 | 13 | 645 | 54 | 32 | 4.57 |
| 132 | Guadeloupe | 53 | 0 | 0 | 53 | 44 | 6.29 |
| 133 | Bahamas | 78 | 11 | 15 | 52 | 35 | 5.00 |
| 134 | Guinea-Bissau | 52 | 0 | 3 | 49 | 32 | 4.57 |
| 135 | Monaco | 94 | 4 | 42 | 48 | 57 | 8.14 |
| 136 | Vietnam | 270 | 0 | 225 | 45 | 94 | 13.43 |
| 137 | Reunion | 45 | 0 | 0 | 45 | 46 | 6.57 |
| 138 | Eswatini | 56 | 1 | 10 | 45 | 43 | 6.14 |
| 139 | Zambia | 84 | 3 | 37 | 44 | 39 | 5.57 |
| 140 | Barbados | 79 | 6 | 31 | 42 | 40 | 5.71 |
| 141 | Libya | 61 | 2 | 18 | 41 | 33 | 4.71 |
| 142 | Nepal | 49 | 0 | 12 | 37 | 92 | 13.14 |
| 143 | Chad | 46 | 0 | 15 | 31 | 38 | 5.43 |
| 144 | Martinique | 32 | 1 | 0 | 31 | 50 | 7.14 |
| 145 | Taiwan | 47 | 1 | 17 | 29 | 95 | 13.57 |
| 146 | Uganda | 75 | 0 | 46 | 29 | 36 | 5.14 |
| 147 | Syria | 42 | 3 | 11 | 28 | 35 | 5.00 |
| 148 | Mongolia | 37 | 0 | 9 | 28 | 47 | 6.71 |
| 149 | Togo | 96 | 6 | 62 | 28 | 51 | 7.29 |
| 150 | Mauritius | 331 | 9 | 295 | 27 | 39 | 5.57 |
| 151 | Benin | 54 | 1 | 27 | 26 | 41 | 5.86 |
| 152 | Eritrea | 39 | 0 | 13 | 26 | 36 | 5.14 |
| 153 | Malawi | 33 | 3 | 4 | 26 | 24 | 3.43 |
| 154 | Zimbabwe | 31 | 4 | 2 | 25 | 37 | 5.29 |
| 155 | occupied Palestinian territory | 25 | 0 | 0 | 25 | 47 | 6.71 |
| 156 | Liechtenstein | 81 | 1 | 55 | 25 | 53 | 7.57 |
| 157 | Timor-Leste | 24 | 0 | 2 | 22 | 35 | 5.00 |
| 158 | Palestine | 22 | 0 | 0 | 22 | 52 | 7.43 |
| 159 | Republic of Ireland | 21 | 0 | 0 | 21 | 49 | 7.00 |
| 160 | Botswana | 22 | 1 | 0 | 21 | 27 | 3.86 |
| 161 | Angola | 25 | 2 | 6 | 17 | 37 | 5.29 |
| 162 | Macau | 45 | 0 | 28 | 17 | 95 | 13.57 |
| 163 | Brunei | 138 | 1 | 121 | 16 | 48 | 6.86 |
| 164 | Saint Kitts and Nevis | 15 | 0 | 2 | 13 | 32 | 4.57 |
| 165 | French Guiana | 18 | 0 | 6 | 12 | 50 | 7.14 |
| 166 | Laos | 19 | 0 | 7 | 12 | 33 | 4.71 |
| 167 | Belize | 18 | 2 | 5 | 11 | 34 | 4.86 |
| 168 | Grenada | 18 | 0 | 7 | 11 | 35 | 5.00 |
| 169 | Antigua and Barbuda | 24 | 3 | 11 | 10 | 44 | 6.29 |
| 170 | Namibia | 16 | 0 | 7 | 9 | 43 | 6.14 |
| 171 | Saint Vincent and the Grenadines | 14 | 0 | 5 | 9 | 43 | 6.14 |
| 172 | Israel | 8 | 0 | 0 | 8 | 65 | 9.29 |
| 173 | Papua New Guinea | 8 | 0 | 0 | 8 | 37 | 5.29 |
| 174 | Fiji | 18 | 0 | 10 | 8 | 38 | 5.43 |
| 175 | Holy See | 9 | 0 | 2 | 7 | 47 | 6.71 |
| 176 | MS Zaandam | 9 | 2 | 0 | 7 | 29 | 4.14 |
| 177 | Mayotte | 7 | 0 | 0 | 7 | 41 | 5.86 |
| 178 | Burundi | 11 | 1 | 4 | 6 | 26 | 3.71 |
| 179 | Central African Republic | 16 | 0 | 10 | 6 | 42 | 6.00 |
| 180 | South Sudan | 5 | 0 | 0 | 5 | 21 | 3.00 |
| 181 | Germany | 5 | 0 | 0 | 5 | 89 | 12.71 |
| 182 | Seychelles | 11 | 0 | 6 | 5 | 43 | 6.14 |
| 183 | Cambodia | 122 | 0 | 117 | 5 | 90 | 12.86 |
| 184 | Aruba | 4 | 0 | 0 | 4 | 44 | 6.29 |
| 185 | Sao Tome and Principe | 4 | 0 | 0 | 4 | 20 | 2.86 |
| 186 | Bahamas, The | 4 | 0 | 0 | 4 | 38 | 5.43 |
| 187 | Bhutan | 7 | 0 | 3 | 4 | 51 | 7.29 |
| 188 | Dominica | 16 | 0 | 13 | 3 | 35 | 5.00 |
| 189 | Saint Barthelemy | 3 | 0 | 0 | 3 | 53 | 7.57 |
| 190 | Puerto Rico | 3 | 0 | 0 | 3 | 41 | 5.86 |
| 191 | Guam | 3 | 0 | 0 | 3 | 41 | 5.86 |
| 192 | Faroe Islands | 2 | 0 | 0 | 2 | 53 | 7.57 |
| 193 | St. Martin | 2 | 0 | 0 | 2 | 48 | 6.86 |
| 194 | Jersey | 2 | 0 | 0 | 2 | 43 | 6.14 |
| 195 | Nicaragua | 12 | 3 | 7 | 2 | 38 | 5.43 |
| 196 | Suriname | 10 | 1 | 7 | 2 | 43 | 6.14 |
| 197 | Lebanon | 2 | 0 | 0 | 2 | 65 | 9.29 |
| 198 | ('St. Martin',) | 2 | 0 | 0 | 2 | 47 | 6.71 |
| 199 | Guernsey | 1 | 0 | 0 | 1 | 43 | 6.14 |
| 200 | North Ireland | 1 | 0 | 0 | 1 | 58 | 8.29 |
| 201 | Western Sahara | 6 | 0 | 5 | 1 | 21 | 3.00 |
| 202 | Republic of the Congo | 1 | 0 | 0 | 1 | 41 | 5.86 |
| 203 | Vatican City | 1 | 0 | 0 | 1 | 51 | 7.29 |
| 204 | Gambia, The | 1 | 0 | 0 | 1 | 39 | 5.57 |
| 205 | Gambia | 10 | 1 | 8 | 1 | 35 | 5.00 |
| 206 | Cape Verde | 1 | 0 | 0 | 1 | 36 | 5.14 |
| 207 | Cayman Islands | 1 | 0 | 0 | 1 | 44 | 6.29 |
| 208 | Greenland | 1 | 0 | 0 | 1 | 41 | 5.86 |
| 209 | Channel Islands | 1 | 0 | 0 | 1 | 47 | 6.71 |
| 210 | Curacao | 1 | 0 | 0 | 1 | 43 | 6.14 |
| 211 | The Gambia | 1 | 0 | 0 | 1 | 40 | 5.71 |
| 212 | The Bahamas | 1 | 0 | 0 | 1 | 41 | 5.86 |
| 213 | East Timor | 1 | 0 | 0 | 1 | 36 | 5.14 |
| 214 | Azerbaijan | 1 | 0 | 0 | 1 | 58 | 8.29 |
| 215 | Saint Lucia | 15 | 0 | 15 | 0 | 43 | 6.14 |
| 216 | Mauritania | 7 | 1 | 6 | 0 | 43 | 6.14 |
| 217 | Gibraltar | 1 | 0 | 1 | 0 | 53 | 7.57 |
| 218 | Yemen | 1 | 0 | 1 | 0 | 16 | 2.29 |
| 219 | Mainland China | 82827 | 4632 | 78225 | -30 | 95 | 13.57 |
dfww_recovered['Recovered Percent'] = round((dfww_recovered['Recovered'] / dfww_recovered['Confirmed']), 4)
dfww_recovered = dfww_recovered[['Country/Region',
'Confirmed', 'Deaths', 'Recovered',
'Active', 'Recovered Percent',
'Days Since 1st Case', 'Weeks Since 1st Case']]
dfww_recovered = dfww_recovered.loc[dfww_recovered['Confirmed'] > 2000, :]
dfww_recovered.sort_values(by='Recovered Percent', ascending=False)\
.reset_index(drop=True).style.background_gradient(cmap='Greens').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
'Recovered': '{:.0f}', 'Active': '{:.0f}',
'Recovered Percent': '{:.2%}', 'Weeks Since 1st Case': '{:.2f}'})
| Country/Region | Confirmed | Deaths | Recovered | Active | Recovered Percent | Days Since 1st Case | Weeks Since 1st Case | |
|---|---|---|---|---|---|---|---|---|
| 0 | Mainland China | 82827 | 4632 | 78225 | -30 | 94.44% | 95 | 13.57 |
| 1 | Thailand | 2907 | 51 | 2547 | 309 | 87.62% | 95 | 13.57 |
| 2 | Luxembourg | 3711 | 85 | 3088 | 538 | 83.21% | 57 | 8.14 |
| 3 | South Korea | 10728 | 242 | 8717 | 1769 | 81.25% | 95 | 13.57 |
| 4 | Austria | 15148 | 536 | 12103 | 2509 | 79.90% | 61 | 8.71 |
| 5 | Australia | 6694 | 80 | 5271 | 1343 | 78.74% | 92 | 13.14 |
| 6 | Iran | 89328 | 5650 | 68193 | 15485 | 76.34% | 67 | 9.57 |
| 7 | Switzerland | 28894 | 1599 | 21300 | 5995 | 73.72% | 61 | 8.71 |
| 8 | Malaysia | 5742 | 98 | 3762 | 1882 | 65.52% | 92 | 13.14 |
| 9 | Finland | 4475 | 186 | 2500 | 1789 | 55.87% | 88 | 12.57 |
| 10 | Chile | 12858 | 181 | 6746 | 5931 | 52.47% | 54 | 7.71 |
| 11 | Mexico | 13842 | 1305 | 7149 | 5388 | 51.65% | 58 | 8.29 |
| 12 | Croatia | 2016 | 54 | 1034 | 928 | 51.29% | 61 | 8.71 |
| 13 | Ireland | 18561 | 1063 | 9233 | 8265 | 49.74% | 57 | 8.14 |
| 14 | Brazil | 59324 | 4057 | 29160 | 26107 | 49.15% | 60 | 8.57 |
| 15 | Algeria | 3256 | 419 | 1479 | 1358 | 45.42% | 61 | 8.71 |
| 16 | Bahrain | 2588 | 8 | 1160 | 1420 | 44.82% | 62 | 8.86 |
| 17 | Spain | 223759 | 22902 | 95708 | 105149 | 42.77% | 85 | 12.14 |
| 18 | South Africa | 4361 | 86 | 1473 | 2802 | 33.78% | 52 | 7.43 |
| 19 | Czech Republic | 7352 | 218 | 2453 | 4681 | 33.37% | 56 | 8.00 |
| 20 | Italy | 195351 | 26384 | 63120 | 105847 | 32.31% | 86 | 12.29 |
| 21 | Peru | 25331 | 700 | 7797 | 16834 | 30.78% | 51 | 7.29 |
| 22 | Argentina | 3780 | 185 | 1030 | 2565 | 27.25% | 54 | 7.71 |
| 23 | Romania | 10635 | 601 | 2890 | 7144 | 27.17% | 60 | 8.57 |
| 24 | Egypt | 4319 | 307 | 1114 | 2898 | 25.79% | 72 | 10.29 |
| 25 | Moldova | 3304 | 94 | 825 | 2385 | 24.97% | 49 | 7.00 |
| 26 | Kazakhstan | 2601 | 25 | 646 | 1930 | 24.84% | 44 | 6.29 |
| 27 | Turkey | 107773 | 2706 | 25582 | 79485 | 23.74% | 46 | 6.57 |
| 28 | Greece | 2506 | 130 | 577 | 1799 | 23.02% | 60 | 8.57 |
| 29 | Belgium | 45325 | 6917 | 10417 | 27991 | 22.98% | 82 | 11.71 |
| 30 | Kuwait | 2892 | 19 | 656 | 2217 | 22.68% | 62 | 8.86 |
| 31 | India | 26283 | 825 | 5939 | 19519 | 22.60% | 87 | 12.43 |
| 32 | Pakistan | 12723 | 269 | 2866 | 9588 | 22.53% | 60 | 8.57 |
| 33 | Colombia | 5142 | 233 | 1067 | 3842 | 20.75% | 51 | 7.29 |
| 34 | United Arab Emirates | 9813 | 71 | 1887 | 7855 | 19.23% | 88 | 12.57 |
| 35 | Poland | 11273 | 524 | 2126 | 8623 | 18.86% | 53 | 7.57 |
| 36 | Hungary | 2443 | 262 | 458 | 1723 | 18.75% | 53 | 7.57 |
| 37 | Belarus | 9590 | 67 | 1573 | 7950 | 16.40% | 58 | 8.29 |
| 38 | Dominican Republic | 5926 | 273 | 822 | 4831 | 13.87% | 56 | 8.00 |
| 39 | Morocco | 3897 | 159 | 537 | 3201 | 13.78% | 55 | 7.86 |
| 40 | Saudi Arabia | 16299 | 136 | 2215 | 13948 | 13.59% | 55 | 7.86 |
| 41 | Serbia | 6630 | 125 | 870 | 5635 | 13.12% | 51 | 7.29 |
| 42 | UK | 6668 | 303 | 857 | 5508 | 12.85% | 86 | 12.29 |
| 43 | Japan | 13231 | 360 | 1656 | 11215 | 12.52% | 95 | 13.57 |
| 44 | Indonesia | 8607 | 720 | 1042 | 6845 | 12.11% | 55 | 7.86 |
| 45 | Philippines | 7294 | 494 | 792 | 6008 | 10.86% | 87 | 12.43 |
| 46 | US | 939634 | 53786 | 101141 | 784707 | 10.76% | 95 | 13.57 |
| 47 | Qatar | 9358 | 10 | 929 | 8419 | 9.93% | 57 | 8.14 |
| 48 | Ukraine | 8125 | 201 | 782 | 7142 | 9.62% | 54 | 7.71 |
| 49 | Russia | 74588 | 681 | 6250 | 67657 | 8.38% | 86 | 12.29 |
| 50 | France | 35456 | 1456 | 2949 | 31051 | 8.32% | 93 | 13.29 |
| 51 | Singapore | 12693 | 12 | 1002 | 11679 | 7.89% | 94 | 13.43 |
| 52 | Panama | 5538 | 159 | 338 | 5041 | 6.10% | 47 | 6.71 |
| 53 | Ecuador | 22719 | 576 | 1366 | 20777 | 6.01% | 56 | 8.00 |
| 54 | Sweden | 18177 | 2192 | 1005 | 14980 | 5.53% | 86 | 12.29 |
| 55 | Portugal | 23392 | 880 | 1277 | 21235 | 5.46% | 55 | 7.86 |
| 56 | Netherlands | 3825 | 151 | 104 | 3570 | 2.72% | 59 | 8.43 |
| 57 | Bangladesh | 4998 | 140 | 113 | 4745 | 2.26% | 49 | 7.00 |
| 58 | Norway | 7499 | 201 | 32 | 7266 | 0.43% | 60 | 8.57 |
| 59 | Canada | 46357 | 2565 | 14 | 43778 | 0.03% | 91 | 13.00 |
dfww_deaths['Mortality Rate'] = round((dfww_deaths['Deaths'] / dfww_deaths['Confirmed']), 4)
dfww_deaths = dfww_deaths[['Country/Region',
'Confirmed', 'Deaths', 'Recovered',
'Active', 'Mortality Rate',
'Days Since 1st Case', 'Weeks Since 1st Case']]
dfww_deaths = dfww_deaths.loc[dfww_deaths['Confirmed'] > 2000, :]
dfww_deaths.sort_values(by='Mortality Rate', ascending=False)\
.reset_index(drop=True).style.background_gradient(cmap='YlOrRd').format({'Confirmed': '{:.0f}', 'Deaths': '{:.0f}',
'Recovered': '{:.0f}', 'Active': '{:.0f}',
'Mortality Rate': '{:.02%}', 'Weeks Since 1st Case': '{:.2f}'})
| Country/Region | Confirmed | Deaths | Recovered | Active | Mortality Rate | Days Since 1st Case | Weeks Since 1st Case | |
|---|---|---|---|---|---|---|---|---|
| 0 | Belgium | 45325 | 6917 | 10417 | 27991 | 15.26% | 82 | 11.71 |
| 1 | Italy | 195351 | 26384 | 63120 | 105847 | 13.51% | 86 | 12.29 |
| 2 | Algeria | 3256 | 419 | 1479 | 1358 | 12.87% | 61 | 8.71 |
| 3 | Sweden | 18177 | 2192 | 1005 | 14980 | 12.06% | 86 | 12.29 |
| 4 | Hungary | 2443 | 262 | 458 | 1723 | 10.72% | 53 | 7.57 |
| 5 | Spain | 223759 | 22902 | 95708 | 105149 | 10.24% | 85 | 12.14 |
| 6 | Mexico | 13842 | 1305 | 7149 | 5388 | 9.43% | 58 | 8.29 |
| 7 | Indonesia | 8607 | 720 | 1042 | 6845 | 8.37% | 55 | 7.86 |
| 8 | Egypt | 4319 | 307 | 1114 | 2898 | 7.11% | 72 | 10.29 |
| 9 | Brazil | 59324 | 4057 | 29160 | 26107 | 6.84% | 60 | 8.57 |
| 10 | Philippines | 7294 | 494 | 792 | 6008 | 6.77% | 87 | 12.43 |
| 11 | Iran | 89328 | 5650 | 68193 | 15485 | 6.33% | 67 | 9.57 |
| 12 | Ireland | 18561 | 1063 | 9233 | 8265 | 5.73% | 57 | 8.14 |
| 13 | US | 939634 | 53786 | 101141 | 784707 | 5.72% | 95 | 13.57 |
| 14 | Romania | 10635 | 601 | 2890 | 7144 | 5.65% | 60 | 8.57 |
| 15 | Mainland China | 82827 | 4632 | 78225 | -30 | 5.59% | 95 | 13.57 |
| 16 | Switzerland | 28894 | 1599 | 21300 | 5995 | 5.53% | 61 | 8.71 |
| 17 | Canada | 46357 | 2565 | 14 | 43778 | 5.53% | 91 | 13.00 |
| 18 | Greece | 2506 | 130 | 577 | 1799 | 5.19% | 60 | 8.57 |
| 19 | Argentina | 3780 | 185 | 1030 | 2565 | 4.89% | 54 | 7.71 |
| 20 | Poland | 11273 | 524 | 2126 | 8623 | 4.65% | 53 | 7.57 |
| 21 | Dominican Republic | 5926 | 273 | 822 | 4831 | 4.61% | 56 | 8.00 |
| 22 | UK | 6668 | 303 | 857 | 5508 | 4.54% | 86 | 12.29 |
| 23 | Colombia | 5142 | 233 | 1067 | 3842 | 4.53% | 51 | 7.29 |
| 24 | Finland | 4475 | 186 | 2500 | 1789 | 4.16% | 88 | 12.57 |
| 25 | France | 35456 | 1456 | 2949 | 31051 | 4.11% | 93 | 13.29 |
| 26 | Morocco | 3897 | 159 | 537 | 3201 | 4.08% | 55 | 7.86 |
| 27 | Netherlands | 3825 | 151 | 104 | 3570 | 3.95% | 59 | 8.43 |
| 28 | Portugal | 23392 | 880 | 1277 | 21235 | 3.76% | 55 | 7.86 |
| 29 | Austria | 15148 | 536 | 12103 | 2509 | 3.54% | 61 | 8.71 |
| 30 | India | 26283 | 825 | 5939 | 19519 | 3.14% | 87 | 12.43 |
| 31 | Czech Republic | 7352 | 218 | 2453 | 4681 | 2.97% | 56 | 8.00 |
| 32 | Panama | 5538 | 159 | 338 | 5041 | 2.87% | 47 | 6.71 |
| 33 | Moldova | 3304 | 94 | 825 | 2385 | 2.85% | 49 | 7.00 |
| 34 | Bangladesh | 4998 | 140 | 113 | 4745 | 2.80% | 49 | 7.00 |
| 35 | Peru | 25331 | 700 | 7797 | 16834 | 2.76% | 51 | 7.29 |
| 36 | Japan | 13231 | 360 | 1656 | 11215 | 2.72% | 95 | 13.57 |
| 37 | Norway | 7499 | 201 | 32 | 7266 | 2.68% | 60 | 8.57 |
| 38 | Croatia | 2016 | 54 | 1034 | 928 | 2.68% | 61 | 8.71 |
| 39 | Ecuador | 22719 | 576 | 1366 | 20777 | 2.54% | 56 | 8.00 |
| 40 | Turkey | 107773 | 2706 | 25582 | 79485 | 2.51% | 46 | 6.57 |
| 41 | Ukraine | 8125 | 201 | 782 | 7142 | 2.47% | 54 | 7.71 |
| 42 | Luxembourg | 3711 | 85 | 3088 | 538 | 2.29% | 57 | 8.14 |
| 43 | South Korea | 10728 | 242 | 8717 | 1769 | 2.26% | 95 | 13.57 |
| 44 | Pakistan | 12723 | 269 | 2866 | 9588 | 2.11% | 60 | 8.57 |
| 45 | South Africa | 4361 | 86 | 1473 | 2802 | 1.97% | 52 | 7.43 |
| 46 | Serbia | 6630 | 125 | 870 | 5635 | 1.89% | 51 | 7.29 |
| 47 | Thailand | 2907 | 51 | 2547 | 309 | 1.75% | 95 | 13.57 |
| 48 | Malaysia | 5742 | 98 | 3762 | 1882 | 1.71% | 92 | 13.14 |
| 49 | Chile | 12858 | 181 | 6746 | 5931 | 1.41% | 54 | 7.71 |
| 50 | Australia | 6694 | 80 | 5271 | 1343 | 1.20% | 92 | 13.14 |
| 51 | Kazakhstan | 2601 | 25 | 646 | 1930 | 0.96% | 44 | 6.29 |
| 52 | Russia | 74588 | 681 | 6250 | 67657 | 0.91% | 86 | 12.29 |
| 53 | Saudi Arabia | 16299 | 136 | 2215 | 13948 | 0.83% | 55 | 7.86 |
| 54 | United Arab Emirates | 9813 | 71 | 1887 | 7855 | 0.72% | 88 | 12.57 |
| 55 | Belarus | 9590 | 67 | 1573 | 7950 | 0.70% | 58 | 8.29 |
| 56 | Kuwait | 2892 | 19 | 656 | 2217 | 0.66% | 62 | 8.86 |
| 57 | Bahrain | 2588 | 8 | 1160 | 1420 | 0.31% | 62 | 8.86 |
| 58 | Qatar | 9358 | 10 | 929 | 8419 | 0.11% | 57 | 8.14 |
| 59 | Singapore | 12693 | 12 | 1002 | 11679 | 0.09% | 94 | 13.43 |
italy = covid.loc[covid["Country/Region"] == "Italy", :]
italy = italy.reset_index(drop=True)
#Create DataFrame of days with Confirmed Cases
italy_temp = italy[italy['Confirmed']>0]
italy_temp = italy_temp.reset_index(drop=True)
#Find start date of infections in YYY-DD-MM format
italy_start = italy_temp['Date'].reset_index(drop=True)[0]
#Find todays date in YYYY-DD-MM format
today = dt.datetime.today()
#Days since first infection date
difference = today - italy_start
difference = difference.days
difference
#Find Daily increase in Confirmed, Deaths, and Recovered
it_confirmed_pct_change = round(italy['Confirmed'].pct_change() * 100, 2)
it_death_change = round(italy['Deaths'].pct_change() * 100, 2)
it_recovery_change = round(italy['Recovered'].pct_change() * 100, 2)
#Find Daily Percentage Increase in Confirmed, Deaths, and Recovered
it_confirmed_perday = italy['Confirmed'].diff()
it_death_perday = italy['Deaths'].diff()
it_recovered_perday = italy['Recovered'].diff()
italy_percent_change = pd.DataFrame
italy_percent = pd.DataFrame({
"New Confirmed Cases Per Day": it_confirmed_perday,
"Confirmed Percent Change" : it_confirmed_pct_change,
"New Confirmed Deaths Per Day": it_death_perday,
"Death Percent Change" : it_death_change,
"New Recovered Cases Per Day": it_recovered_perday,
"Recovery Percent Change" : it_recovery_change,
})
italy_percent.fillna(value=0)
| New Confirmed Cases Per Day | Confirmed Percent Change | New Confirmed Deaths Per Day | Death Percent Change | New Recovered Cases Per Day | Recovery Percent Change | |
|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 1 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 2 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 3 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 4 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| ... | ... | ... | ... | ... | ... | ... |
| 81 | 2729.0 | 1.51 | 534.0 | 2.21 | 2723.0 | 5.57 |
| 82 | 3370.0 | 1.83 | 437.0 | 1.77 | 2943.0 | 5.70 |
| 83 | 2646.0 | 1.41 | 464.0 | 1.85 | 3033.0 | 5.56 |
| 84 | 3021.0 | 1.59 | 420.0 | 1.64 | 2922.0 | 5.08 |
| 85 | 2357.0 | 1.22 | 415.0 | 1.60 | 2622.0 | 4.33 |
86 rows × 6 columns
#Merge DataSets together into 1 df
italy_df = italy.merge(italy_percent, left_index=True, right_index=True)
italy_df = italy_df.fillna(value=0)
italy_df
| Province/State | Country/Region | Date | Confirmed | Deaths | Recovered | New Confirmed Cases Per Day | Confirmed Percent Change | New Confirmed Deaths Per Day | Death Percent Change | New Recovered Cases Per Day | Recovery Percent Change | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Italy | 2020-01-31 | 2.0 | 0.0 | 0.0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 1 | 0 | Italy | 2020-02-01 | 2.0 | 0.0 | 0.0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 2 | 0 | Italy | 2020-02-02 | 2.0 | 0.0 | 0.0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 3 | 0 | Italy | 2020-02-03 | 2.0 | 0.0 | 0.0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 4 | 0 | Italy | 2020-02-04 | 2.0 | 0.0 | 0.0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 81 | 0 | Italy | 2020-04-21 | 183957.0 | 24648.0 | 51600.0 | 2729.0 | 1.51 | 534.0 | 2.21 | 2723.0 | 5.57 |
| 82 | 0 | Italy | 2020-04-22 | 187327.0 | 25085.0 | 54543.0 | 3370.0 | 1.83 | 437.0 | 1.77 | 2943.0 | 5.70 |
| 83 | 0 | Italy | 2020-04-23 | 189973.0 | 25549.0 | 57576.0 | 2646.0 | 1.41 | 464.0 | 1.85 | 3033.0 | 5.56 |
| 84 | 0 | Italy | 2020-04-24 | 192994.0 | 25969.0 | 60498.0 | 3021.0 | 1.59 | 420.0 | 1.64 | 2922.0 | 5.08 |
| 85 | 0 | Italy | 2020-04-25 | 195351.0 | 26384.0 | 63120.0 | 2357.0 | 1.22 | 415.0 | 1.60 | 2622.0 | 4.33 |
86 rows × 12 columns
fig = px.bar(italy_df, x='Date', y='New Confirmed Deaths Per Day',
hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Confirmed Deaths Per Day',
height=400)
fig.show()
fig = px.bar(italy_df, x='Date', y='New Confirmed Cases Per Day',
hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Confirmed Cases Per Day',
height=400)
fig.show()
us = covid.loc[covid["Country/Region"] == "US", :]
us = us.reset_index(drop=True)
us
#Create DataFrame of days with Confirmed Cases
us_temp = us[us['Confirmed']>0]
us_temp = us_temp.reset_index(drop=True)
us = covid.loc[covid["Country/Region"] == "US", :]
us = us.reset_index(drop=True)
#Group by Date and not state
us_df = us.groupby(by="Date").agg('sum').reset_index(drop=False)
#Create DataFrame of days with Confirmed Cases
us_temp = us_df[us_df['Confirmed']>0]
us_temp = us_temp.reset_index(drop=True)
#Find start date of infections in YYY-DD-MM format
us_start = us_temp['Date'].reset_index(drop=True)[0]
#Find todays date in YYYY-DD-MM format
today = dt.datetime.today()
#Days since first infection date
difference = today - us_start
difference = difference.days
difference
#Find Daily increase in Confirmed, Deaths, and Recovered
us_confirmed_pct_change = round(us_df['Confirmed'].pct_change() * 100, 2)
us_death_change = round(us_df['Deaths'].pct_change() * 100, 2)
us_recovery_change = round(us_df['Recovered'].pct_change() * 100, 2)
#Find Daily Percentage Increase in Confirmed, Deaths, and Recovered
us_confirmed_perday = us_df['Confirmed'].diff()
us_death_perday = us_df['Deaths'].diff()
us_recovered_perday = us_df['Recovered'].diff()
us_percent_change = pd.DataFrame
us_percent = pd.DataFrame({
"New Confirmed Cases Per Day": us_confirmed_perday,
"Confirmed Percent Change" : us_confirmed_pct_change,
"New Confirmed Deaths Per Day": us_death_perday,
"Death Percent Change" : us_death_change,
"New Recovered Cases Per Day": us_recovered_perday,
"Recovery Percent Change" : us_recovery_change,
})
us_percent.fillna(value=0)
| New Confirmed Cases Per Day | Confirmed Percent Change | New Confirmed Deaths Per Day | Death Percent Change | New Recovered Cases Per Day | Recovery Percent Change | |
|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 1 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 2 | 1.0 | 100.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 3 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 4 | 3.0 | 150.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| ... | ... | ... | ... | ... | ... | ... |
| 90 | 27539.0 | 3.51 | 2350.0 | 5.58 | 2875.0 | 3.97 |
| 91 | 28355.0 | 3.49 | 2178.0 | 4.90 | 2162.0 | 2.87 |
| 92 | 28950.0 | 3.45 | 3332.0 | 7.15 | 2837.0 | 3.67 |
| 93 | 36163.0 | 4.16 | 1995.0 | 3.99 | 18876.0 | 23.54 |
| 94 | 32821.0 | 3.63 | 1806.0 | 3.48 | 1293.0 | 1.31 |
95 rows × 6 columns
#Merge DataSets together into 1 df
us_df = us_df.merge(us_percent, left_index=True, right_index=True)
us_df = us_df.fillna(value=0)
fig = px.bar(us_df, x='Date', y='New Confirmed Cases Per Day',
hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Confirmed Cases Per Day',
height=400)
fig.show()
fig = px.bar(us_df, x='Date', y='New Confirmed Deaths Per Day',
hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Confirmed Deaths Per Day',
height=400)
fig.show()
fig = px.bar(us_df, x='Date', y='New Recovered Cases Per Day',
hover_data=['Confirmed', 'Deaths', 'Recovered'], color='New Recovered Cases Per Day',
height=400)
fig.show()
fig = px.bar(us_df, x='Date', y='Confirmed Percent Change',
hover_data=['Confirmed', 'Deaths', 'Recovered'], color='Confirmed Percent Change',
height=400)
fig.show()
us_df
| Date | Confirmed | Deaths | Recovered | New Confirmed Cases Per Day | Confirmed Percent Change | New Confirmed Deaths Per Day | Death Percent Change | New Recovered Cases Per Day | Recovery Percent Change | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2020-01-22 | 1.0 | 0.0 | 0.0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 1 | 2020-01-23 | 1.0 | 0.0 | 0.0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 2 | 2020-01-24 | 2.0 | 0.0 | 0.0 | 1.0 | 100.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 3 | 2020-01-25 | 2.0 | 0.0 | 0.0 | 0.0 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| 4 | 2020-01-26 | 5.0 | 0.0 | 0.0 | 3.0 | 150.00 | 0.0 | 0.00 | 0.0 | 0.00 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 90 | 2020-04-21 | 811865.0 | 44444.0 | 75204.0 | 27539.0 | 3.51 | 2350.0 | 5.58 | 2875.0 | 3.97 |
| 91 | 2020-04-22 | 840220.0 | 46622.0 | 77366.0 | 28355.0 | 3.49 | 2178.0 | 4.90 | 2162.0 | 2.87 |
| 92 | 2020-04-23 | 869170.0 | 49954.0 | 80203.0 | 28950.0 | 3.45 | 3332.0 | 7.15 | 2837.0 | 3.67 |
| 93 | 2020-04-24 | 905333.0 | 51949.0 | 99079.0 | 36163.0 | 4.16 | 1995.0 | 3.99 | 18876.0 | 23.54 |
| 94 | 2020-04-25 | 938154.0 | 53755.0 | 100372.0 | 32821.0 | 3.63 | 1806.0 | 3.48 | 1293.0 | 1.31 |
95 rows × 10 columns
fig = px.bar(us_df, x='Date', y='Death Percent Change',
hover_data=['Confirmed', 'Deaths', 'Recovered'], color='Death Percent Change',
height=400)
fig.show()
fig = px.bar(us_df, x='Date', y='Death Percent Change',
hover_data=['Confirmed', 'Deaths', 'Recovered'], color='Death Percent Change',
height=400)
fig.show()
covid_2['Date'] = covid_2['Date'].dt.strftime('%Y-%m-%d')
fig = px.area(covid_2, x="Date", y="Confirmed", color="Country", line_group="Country")
fig.show()
fig = px.area(covid_2, x="Date", y="Deaths", color="Country", line_group="Country")
fig.show()
# covid_2['Date'] = covid_2['Date'].dt.strftime('%Y-%m-%d')
#ADD ISO_CODES for Choropleth
input_countries = covid_2['Country']
countries = {}
for country in pycountry.countries:
countries[country.name] = country.alpha_3
codes = [countries.get(country, 'Unknown code') for country in input_countries]
#turn into dataframe
codes_df = pd.DataFrame(codes)
#concat
covid_iso = pd.concat([covid_2, codes_df], axis=1)
#Clean-up
covid_iso = covid_iso.rename(columns={0: 'iso_code'})
covid_iso.sort_values(by='Confirmed', ascending=False)
covid_iso.loc[covid_iso['Country'] == 'US' , 'iso_code'] = 'USA'
covid_iso.loc[covid_iso['Country'] == 'Mainland China' , 'iso_code'] = 'CHN'
covid_iso.loc[covid_iso['Country'] == 'UK' , 'iso_code'] = 'GBR'
covid_iso.loc[covid_iso['Country'] == 'Russia', 'iso_code'] = 'RUS'
covid_iso.loc[covid_iso['Country'] == 'South Korea', 'iso_code'] = 'KOR'
covid_iso.loc[covid_iso['Country'] == 'Macau', 'iso_code'] = 'MAC'
covid_iso.loc[covid_iso['Country'] == 'Taiwan', 'iso_code'] = 'TWN'
covid_iso.loc[covid_iso['Country'] == 'Venezuela', 'iso_code'] = 'VEN'
covid_iso.loc[covid_iso['Country'] == 'Vietnam', 'iso_code'] = 'VNM'
covid_iso.loc[covid_iso['Country'] == 'Syria', 'iso_code'] = 'SYR'
covid_iso.loc[covid_iso['Country'] == 'Tanzania', 'iso_code'] = 'TZA'
covid_iso.loc[covid_iso['Country'] == 'Kosovo', 'iso_code'] = 'RKS'
covid_iso.loc[covid_iso['Country'] == 'West Bank and Gaza', 'iso_code'] = 'PSE'
covid_iso.loc[covid_iso['Country'] == 'Iran', 'iso_code'] = 'IRN'
covid_iso.sort_values(by='Confirmed', ascending=False)
try:
covid_iso['Mortality Rate'] = round(covid_iso['Deaths'] / covid_iso['Confirmed'], 2) *100
except ZeroDivisionError:
covid_iso['Mortality Rate'] = 0
covid_iso[covid_iso['iso_code'] == 'Unknown code']
| Date | Country | Confirmed | Deaths | Recovered | iso_code | Mortality Rate | |
|---|---|---|---|---|---|---|---|
| 69 | 2020-01-27 | Ivory Coast | 1.0 | 0.0 | 0.0 | Unknown code | 0.0 |
| 348 | 2020-02-07 | Others | 61.0 | 0.0 | 0.0 | Unknown code | 0.0 |
| 377 | 2020-02-08 | Others | 61.0 | 0.0 | 0.0 | Unknown code | 0.0 |
| 406 | 2020-02-09 | Others | 64.0 | 0.0 | 0.0 | Unknown code | 0.0 |
| 435 | 2020-02-10 | Others | 135.0 | 0.0 | 0.0 | Unknown code | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 10051 | 2020-04-25 | Holy See | 9.0 | 0.0 | 2.0 | Unknown code | 0.0 |
| 10063 | 2020-04-25 | Ivory Coast | 1077.0 | 14.0 | 419.0 | Unknown code | 1.0 |
| 10072 | 2020-04-25 | Laos | 19.0 | 0.0 | 7.0 | Unknown code | 0.0 |
| 10080 | 2020-04-25 | MS Zaandam | 9.0 | 2.0 | 0.0 | Unknown code | 22.0 |
| 10092 | 2020-04-25 | Moldova | 3304.0 | 94.0 | 825.0 | Unknown code | 3.0 |
619 rows × 7 columns
fig = px.choropleth(covid_iso, locations="iso_code", color="Confirmed", hover_name="Country", animation_frame="Date", range_color=[0,150000])
fig.show()
fig = px.choropleth(covid_iso, locations="iso_code", color="Mortality Rate", hover_name="Country", animation_frame="Date", range_color=[0,8])
fig.show()
usa = covid[covid['Country/Region'] == 'US'].reset_index(drop=True)
usa = usa.rename(columns={'Province/State': 'State',
'Country/Region': 'Country'})
usa
| State | Country | Date | Confirmed | Deaths | Recovered | |
|---|---|---|---|---|---|---|
| 0 | Washington | US | 2020-01-22 | 1.0 | 0.0 | 0.0 |
| 1 | Washington | US | 2020-01-23 | 1.0 | 0.0 | 0.0 |
| 2 | Washington | US | 2020-01-24 | 1.0 | 0.0 | 0.0 |
| 3 | Chicago | US | 2020-01-24 | 1.0 | 0.0 | 0.0 |
| 4 | Washington | US | 2020-01-25 | 1.0 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... |
| 3593 | Virginia | US | 2020-04-25 | 12366.0 | 437.0 | 0.0 |
| 3594 | Washington | US | 2020-04-25 | 13319.0 | 737.0 | 0.0 |
| 3595 | West Virginia | US | 2020-04-25 | 1010.0 | 32.0 | 0.0 |
| 3596 | Wisconsin | US | 2020-04-25 | 5687.0 | 266.0 | 0.0 |
| 3597 | Wyoming | US | 2020-04-25 | 491.0 | 7.0 | 0.0 |
3598 rows × 6 columns